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Mobile devices are the prevalent computing device in many parts of the world, and over the coming years it is expected that mobile Internet usage will outpace desktop usage worldwide. Google is committed to realizing the potential of the mobile web to transform how people interact with computing technology. Google engineers and researchers work on a wide range of problems in mobile computing and networking, including new operating systems and programming platforms (such as Android and ChromeOS); new interaction paradigms between people and devices; advanced wireless communications; and optimizing the web for mobile settings. In addition, many of Google’s core product teams, such as Search, Gmail, and Maps, have groups focused on optimizing the mobile experience, making it faster and more seamless. We take a cross-layer approach to research in mobile systems and networking, cutting across applications, networks, operating systems, and hardware. The tremendous scale of Google’s products and the Android and Chrome platforms make this a very exciting place to work on these problems.

Some representative projects include mobile web performance optimization, new features in Android to greatly reduce network data usage and energy consumption; new platforms for developing high performance web applications on mobile devices; wireless communication protocols that will yield vastly greater performance over today’s standards; and multi-device interaction based on Android, which is now available on a wide variety of consumer electronics.

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Research Article

Privacy and data protection in mobile cloud computing: A systematic mapping study

Roles Conceptualization, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Faculty of Computing Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia, Faculty of Computer Science and Information Technology, Albaha University, Albaha, Saudi Arabia

Roles Conceptualization, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Computing Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

Roles Methodology, Writing – original draft, Writing – review & editing

• Hussain Mutlaq Alnajrani, 
• Azah Anir Norman, 
• Babiker Hussien Ahmed

• Published: June 11, 2020
• https://doi.org/10.1371/journal.pone.0234312
• Reader Comments

As a result of a shift in the world of technology, the combination of ubiquitous mobile networks and cloud computing produced the mobile cloud computing (MCC) domain. As a consequence of a major concern of cloud users, privacy and data protection are getting substantial attention in the field. Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, however, no specific investigation highlights the results of the existing studies in privacy and data protection. In addition, there are no particular exploration highlights trends and open issues in the domain. Accordingly, the objective of this paper is to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues. In this investigation, a systematic mapping study was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a collection of 74 primary studies were selected. As a result, the present data privacy threats, attacks, and solutions were identified. Also, the ongoing trends of data privacy exercise were observed. Moreover, the most utilized measures, research type, and contribution type facets were emphasized. Additionally, the current open research issues in privacy and data protection in MCC were highlighted. Furthermore, the results demonstrate the current state-of-the-art of privacy and data protection in MCC, and the conclusion will help to identify research trends and open issues in MCC for researchers and offer useful information in MCC for practitioners.

Citation: Alnajrani HM, Norman AA, Ahmed BH (2020) Privacy and data protection in mobile cloud computing: A systematic mapping study. PLoS ONE 15(6): e0234312. https://doi.org/10.1371/journal.pone.0234312

Editor: He Debiao, Wuhan University, CHINA

Received: December 7, 2019; Accepted: May 24, 2020; Published: June 11, 2020

Copyright: © 2020 Alnajrani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

In recent years, mobile cloud computing (MCC) is playing a crucial role in connectivity and accessibility to services and applications [ 1 ]. MCC is a major area of interest evolving out of mobile devices and cloud computing [ 1 – 3 ]. It is an approach that aims to enable mobile terminals to access robust and reliable cloud-based computing that facilitates the optimal utilization of resources.

As an effect of a major concern of cloud users, the issue of privacy and data protection has received considerable attention in the field. A number of researchers have reported that privacy in the definition adopted by the organization for Economic Cooperation and Development [ 4 ] is “any information relating to a recognized or identifiable individual (data subject).” In fact, the concept of privacy has a different perspective, depending on countries, cultures, or jurisdictions.

Recently, researchers have shown an increased interest in MCC. Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, the results of the existing studies in privacy and data protection are not highlighted. Also, no particular research demonstrates the ongoing trends, measures to assess current solutions, and open research issues, including future research directions for privacy and data protection in MCC.

In this study, a systematic mapping study (SMS) was conducted to analyses the existing research literature that addresses privacy and data protection in MCC [ 3 ]. In fact, SMS is a clear and precise method of identifying, evaluating, and explaining all obtainable research relevant to a specific research question, thematic area, or phenomenon of importance [ 3 ]. Furthermore, the purpose of SMS is to present an adjustable, impartial, and reliable assessment of a particular research topic [ 3 ].

The study presented in this paper aims to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues in the domain. In this examination, a systematic mapping study (SMS) was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a collection of 74 primary studies were selected. As a result, the contribution of this study is declared as follows:

• Demonstrate existing threats and attacks on data privacy and solutions to serve personal data.
• Outline metrics and measures that are used to assess the current solutions for privacy in MCC.
• Illustrate the current state-of-the-art of data privacy exercises utilized in MCC and highlight the types of research and contribution areas that are used in mobile cloud computing.
• Highlight open research issues of privacy and data protection in MCC.

This article is constructed as follows: Section 2 presents background and motivation for the study. Section 3 presents the related work. Section 4 describes the research method. Section 5 presents conducting the study. Section 6 shows and discusses the results. Section 7 illustrates the key findings. Section 8 clarifies the thread to the validity. Section 9 presents the conclusion of this study.

2. Background and motivation

This section presents a general background of mobile cloud computing, privacy and data protection, and the needs for a systemic mapping study.

2.1. Mobile cloud computing

Today, mobile devices such as smartphones provide users with greater connectivity and accessibility to services and applications [ 1 ]. Even though mobile technology continues to expand, modern mobile terminals suffer limitations associated with poor computational resources, low memory size, and small disk capacity [ 1 ]. Cloud computing provides a robust approach to the delivery of services by incorporating existing computing technologies. In cloud computing, three service delivery models appear to account for most deployments: Infrastructure-as-a-service (IaaS), Software-as-a-Service (SaaS), and Platform-as-a-Service (PaaS) [ 5 ].

The concept of Mobile Cloud Computing (MCC) has emerged out of mobile technology and cloud computing [ 1 – 3 ]. It is an approach that aims to enable mobile terminals to access robust and reliable cloud-based computing that facilitates the optimal utilization of resources. Moreover, MCC presents opportunities for improving the portability and scalability of services [ 1 ].

2.2. Privacy and data protection

Several researchers have reported that privacy in the definition adopted by the organization for Economic Cooperation and Development [ 4 ] is “any information relating to a recognized or identifiable individual (data subject).” In fact, the concept of privacy is vast and has a different perspective depending on countries, cultures, or jurisdictions.

To be more precise, privacy is not just about hiding information, but it is a legitimate control over personal data since no one may get personal information without the consent of the owner unless there are laws that allow access to such information [ 6 ], for example, income information that the tax authorities can get from employers [ 6 ].

The issue of privacy in MCC is getting nowadays more attention; however, numerous existing privacy laws and regulations are needed to impose the standards for the collection, maintenance, use, and disclosure of personal information that must be satisfied even by cloud providers [ 7 ]. In addition, a number of studies reported that there is always increasing the privacy risk in hosting your data in someone else’s hands [ 7 ].

2.3. The need for a systematic mapping study

Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, our research group has found the following:

• The results of the existing studies in privacy and data protection were not highlighted.
• The ongoing trends in privacy and data protection were not determined.
• The metrics used to assess current solutions were not aggregated.
• The research type facets and the contribution type facets used in MCC were not aggregated.
• The current open research issues with future research directions were not demonstrated.

The aim of this investigation is to highlight the results of the existing studies in privacy and data protection in MCC through a systematic mapping study (SMS). The purpose of a systematic mapping study is to present an adjustable, impartial, and reliable assessment of a particular research topic [ 3 ]. Also, SMS is used to highlight the current state-of-the-art and to determine the trends of the research domain.

3. Related works

In recent years, a number of reviews and surveys have been published to analyze MCC in secondary studies [ 8 – 11 ] and are considered as related to this study. David et al. [ 8 ] focused on the various encryption techniques (and their variants) that are presently being utilized, and on possible future works that could improve privacy-oriented encryption techniques and security. Moreover, the authors tried to provide the audience with a conception about the difficulty of the algorithm being utilized in each of the studied encryption techniques. However, they did not cover other solutions or discuss current attacks and threats related to MCC.

Also, Kulkarni et al. [ 9 ] concentrated on the existing frameworks of MCC, although they did not mention other solutions. In addition, Bhatia and Verma [ 10 ] presented a state-of-the-art organization of cryptographic techniques and data security schemes in an innovative delimitation on chronological order. However, the survey only focused on threats and attacks related to the mobile cloud. Moreover, Rahimi et al. [ 11 ], investigated various security frameworks for the MCC environment, whereby most of them offload processor-heavy jobs to the cloud. The study [ 11 ] suggested some of the challenges that service providers need to address to achieve security and privacy in the MCC environment [ 11 ]. Finally, even though several reviews and surveys have been reported, two limitations remain:

• There is a need for a more systematic way of summarizing the current knowledge in MCC. It is known that the popularity of these studies is as informal literature surveys, which do not include specific research questions, search process, or defined data analysis processor data extractions.
• A few secondary studies focused on privacy and data protection in MCC, while applications based on these platforms continue to multiply.

4. Research method

A systematic mapping study (SMS) is a secondary study that provides a structure of the type of research papers and aggregates the results that have been declared in the domain. Also, SMS is a method for categorizing the published studies, often gives a visual summary, and map the results to highlight the current state-of-the-art and to determine the trends [ 12 ].

In this paper, we have derived the formal guidelines of SMS from Petersen et al. [ 12 ]. As in the directive of SMS [ 12 ], SMS is performed in five steps where the outcome from each step provides the input for the next step. Fig 1 shows the SMS method, as demonstrated in Petersen et al. [ 12 ]. As shown in Fig 1 , SMS is implemented as follows [ 12 ]:

• Step 1 : Define research questions and objectives to provide a general scope for the study.
• Step 2 : Define the search strategy to find the published studies from the available digital libraries.
• Step 3 : Screening process using inclusion and exclusion criteria to choose the relevant studies.
• Step 4 : Keywording to enable classification and data extraction.
• Step 5: Data extraction and mapping process.

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https://doi.org/10.1371/journal.pone.0234312.g001

4.1. Research aim questions and objectives

The study aims to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues in the domain. Table 1 shows our research questions and the objective of each research question.

https://doi.org/10.1371/journal.pone.0234312.t001

4.2. Search strategy

As in the SMS guideline [ 13 ], the primary studies are identified by using a search string [ 13 ] derived from the research questions. An excellent way to create the search string is to structure them in terms of population, intervention, comparison, and outcome (PICO) [ 13 ]. Based on our research questions in Table 1 , PICO is implemented as follows:

• Population : Published studies.
• Intervention : Privacy, data protection, mobile cloud computing, and MCC.
• Comparison : Not applicable.
• Outcome : Published studies in privacy and data protection in mobile cloud computing.

Based on PICO, we constructed our search string as presented in Fig 2 . In this SMS, the search string in Fig 2 is handled to search for studies in the available digital libraries.

https://doi.org/10.1371/journal.pone.0234312.g002

4.3. Inclusion-exclusion criteria

Based on SMS guidelines [ 13 ], applying inclusion and exclusion criteria is crucial to filter the results [ 13 ]. Inclusion and exclusion criteria aim to obtain relevant primary studies to answer the defined research questions [ 13 ]. Table 2 illustrates our inclusion and exclusion criteria.

https://doi.org/10.1371/journal.pone.0234312.t002

4.4 Keywording and classification for data extraction

For the SMS data extraction and classification, the SMS method [ 14 ] declared the following:

• ➢. Ensure that the desired results were covered in the SMS [ 14 ].
• ➢. Aid in introducing a set of categories that represent the underlying population for the study [ 14 ].
• ➢. Develop a high-level understanding of the nature and contribution of the selected primary studies [ 13 ].
• ➢. First, read the abstracts and searched for keywords [ 14 ].
• ➢. Second, identify the context related to the objective of the study and the scheme will be updated [ 14 ].
• Scheme: When having the classification scheme in place, the relevant articles are sorted into the scheme, i.e., the actual data extraction takes place [ 13 ].

As presented in Fig 3 , the classification scheme is implemented as follows:

• Keywording: is the process of reading the abstract and searching for keywords to identify the context related to the objective of the SMS [ 14 ].
• Sort Article into scheme: is the process of sorting the scheme after adding an article into scheme [ 14 ].
• Update scheme: is the process of modifying the scheme after adding a primary study context to the scheme [ 14 ].

https://doi.org/10.1371/journal.pone.0234312.g003

4.5 Data extraction and mapping process

As demonstrated in the SMS method [ 13 ], in this study, we use a data extraction form to gather the SMS data. In addition, when having the classification scheme in place, the actual data extraction of the relevant articles in this study is sorted into the scheme as follows:

• Excel tables were utilized to document the data extraction process [ 13 ].
• The frequencies of publications in each category were analyzed from the final table [ 13 ].

To investigate the trends, as in the SMS method [ 13 ], we focused on the frequencies of publications for each category to identify which categories have been emphasized in past research and thus to identify gaps and possibilities for future research. Also, different ways of presenting and analyzing the results were utilized as follows:

• The summary of the statistics is illustrated in the form of tables, showing the frequencies of publications in each category [ 13 ].
• A bubble plot is illustrated to report the frequencies [ 13 ]. Bubble plot is basically two x-y scatterplots with bubbles in category intersections. The size of a bubble is proportional to the number of articles that are in the pair of categories corresponding to the bubble coordinates [ 13 ].

5. Conducting SMS

In this section, we present the systematic mapping study that we have conducted using the SMS method presented in Section 4.

5.1. Selecting and filtering relevant studies

In this study, we applied PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 15 ] as an evidence-based for reporting the outcome of the search results to clarify the eligible, included or excluded primary studies in this investigation. Fig 4 demonstrates the resulting articles from each database and the screened primary studies for this study using the PRISMA guideline.

https://doi.org/10.1371/journal.pone.0234312.g004

As Fig 4 , in this investigation, five digital databases we selected to search for relevant studies, including IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, and Scopus. Then, we utilized our search string, as presented in Fig 2 , to search for studies in the selected databases. As a result, 1711 studies were obtained and screened as follows:

• By article type: only the studies presented in conferences, magazines, and journals venues initially selected.
• By subject: only the studies related to privacy, data protection, mobile cloud computing, and MCC initially nominated.
• By title: only the studies related to mobile cloud computing initially nominated.

Finally, after screening by year, article type, subject, and title, a total of 215 studies were initially selected and presented in Table 3 .

https://doi.org/10.1371/journal.pone.0234312.t003

In filtering the retrieved studies, a total of 87 studies were excluded based on our inclusion and exclusion criteria ( Table 2 ). Also, 39 duplicated studies were eliminated. In addition, we read a sum of 89 studies in a comprehensive analysis. The comprehensive analysis is a process of reading the whole primary study and decide to include or exclude it after a complete investigation on the actual contribution on exactly and only on the privacy and data protection in mobile cloud computing. Finally, a total of 74 primary studies were selected for SMS. Table 4 shows the results of filtering the retrieved studies.

https://doi.org/10.1371/journal.pone.0234312.t004

5.3. Analysis and classification

In this study, we carried out a classification scheme through keywording as declared in Section 4.4. First, we read the abstracts of the 74 selected primary studies and searched for keywords. In addition, we read the introduction and conclusion sections of each of the selected primary studies to produce the classification scheme. As an outcome, Fig 5 shows our classification scheme.

https://doi.org/10.1371/journal.pone.0234312.g005

As presented in Fig 5 , seven main aspects were defined as follows:

• Data privacy exercises: It denotes the methods of controlling and implementing privacy solutions in mobile cloud computing [ 16 ]. Also, it concerns the demonstration of practice policies of data access using different mechanisms [ 17 ] that governed by the policies of MCC service providers, state regulations and roles.
• ➢. Threat: Potential for infringement of security, which exists when there is a situation, capacity, activity, or occasion that could violate security and cause harm. That is, a risk is a possible peril that may misuse a vulnerability [ 18 ].
• ➢. Attack : A violation of system security that derives from an intelligent threat. This intelligent work is a purposed attempt (especially in the concept of a technique or method) to avoid the security policy of a system and security services [ 18 ].
• Privacy Solutions : These are computational methods serving issues related to authentication, authorization, encryption, access control, and trust.
• Metrics : Privacy metrics are the privacy parameters that are required in measuring the level of privacy in MCC or the privacy service provided by a given solution to MCC [ 19 ].
• Research type : We adopted an existing classification (Wieringa, Maiden, Mead, & Rolland, 2006), which is divided into six classifications: Validation Research, Solution Proposal, Evaluation Research, Philosophical Paper, Opinion Paper, and Experience Paper [ 20 ]. S1 Appendix of Appendix A shows the types of research with the definitions [ 20 ] used in our mapping study.
• Contribution type : For the contribution type facets, we have used the categories from Petersen et al., (2008): Model, Formal Study, Method, System, and Experience [ 20 ]. S1 Appendix of Appendix B shows the definitions of the contribution type facets used in our mapping study.
• Open research issues : is a new challenge noted by the researchers in the existing studies in the area.

6. Results and discussion

In this section, we present and discuss the answers to the research questions of this study.

6.1. RQ1: What are the current data privacy exercises in MCC?

In this study, we have identified eight data privacy exercises; these eight exercises have been highlighted in the selected primary studies for implementing privacy solutions in MCC. Table 5 illustrates the identified data privacy exercises in the selected primary studies.

https://doi.org/10.1371/journal.pone.0234312.t005

In addition, more details are necessary to understand those exercises presented in Table 5 ; those data privacy exercises are defined as follows:

• Setup: is concerning the adaptation of the initial public parameters of system, account, and algorithm for privacy and data protection in MCC [ 26 , 27 ].
• Cryptography: is defined as the method of preserving information by using codes, such that it can only be read and interpreted by those for whom the information is targeted [ 18 ].
• Authentication: it denotes the assurance that the communicating entity is the one that it claims to be [ 18 ].
• Accounts creation: It represents the registration of a mobile device or user to a cloud server is an onetime process wherein the user information (ID, password) are Setup, and some encrypted files are exchanged [ 70 ].
• Verification: is utilized to illustrate the information that corroborates the binding between the entity and the identifier [ 18 ].
• Access control: is the prevention of unauthorized use of a resource [ 18 ].
• Steganography: is used for hiding plaintext messages by concealing the existence of the message [ 18 ].
• Reputation: is one of the components of trustworthiness measures. The reputation establishes based on the recommendations from the MCC users [ 78 ].

Fig 6 shows the percentage of studies related to data privacy exercises based on the number of studies. As presented in Fig 6 , the results show that the selected primary studies focused on setup, cryptography, authentication, account creation, and verification in 25%, 22%, 21%, 14%, and 11% of studies, respectively. On the other hand, access control, steganography, and reputation have scored the lowest percentage with less than 5% each.

https://doi.org/10.1371/journal.pone.0234312.g006

Moreover, Fig 7 is a bubble plot of data privacy exercises in the selected primary studies; the X-axis represents the years, and the Y-axis represents the data privacy exercises. As illustrated in Fig 7 , the number of research rises towards the setup, cryptography, authentication, and accounts creation. Conversely, the number of research decreased towards verification, access control, steganography, and reputation.

https://doi.org/10.1371/journal.pone.0234312.g007

6.2. RQ 2: What are the existing data privacy threats and attacks in MCC?

In this investigation, we have identified 17 data privacy threats and attacks in MCC. Table 6 shows the identified threats and attacks in the selected primary studies.

https://doi.org/10.1371/journal.pone.0234312.t006

Fig 8 displays the percentage of primary studies related to threats and attacks based on the number of studies. As demonstrated in Fig 8 , the most common threats and attacks are unauthorized threats and attacks including users, persons, and access with 18% (34), data privacy with 15% (29), leakage of user privacy 13% (24), data misuse (21) and untrusted service provider (21) represented 11% each. On the other hand, disclosing information or data (11) represented 6%, man-in-the-middle attacks (9) represented 5%, and the rest of the threats got 21%, respectively.

https://doi.org/10.1371/journal.pone.0234312.g008

Furthermore, Fig 9 is a bubble plot of threats and attacks, the X-axis represents the years, and the Y-axis represents the threats and attacks. The results show that unauthorized, data privacy, leakage of user privacy, and phishing attacks are relatively dominant in the field. In contrast, eavesdropping attacks, internal attacks, improper security policies and practices in some locations, internal multi-layer attacks, inference attacks on user privacy, and data breach threats are losing momentum.

https://doi.org/10.1371/journal.pone.0234312.g009

6.3. RQ3: What are the privacy solutions proposed to serve personal data protection in MCC?

As shown in Table 7 , four solutions used to preserve the privacy in MCC in the selected primary studies. The solutions include encryption, authentication, access control, and trust.

https://doi.org/10.1371/journal.pone.0234312.t007

Fig 10 displays the percentage of studies related to privacy solutions based on the number of studies. The outcome shows that the research focused on encryption, authentication, and access control solutions in 50%, 28%, and 19% of studies, respectively. We observed that researchers have started to propose trust as a solution in this domain since we found two studies presented the trust solutions.

https://doi.org/10.1371/journal.pone.0234312.g010

Moreover, Fig 11 is a bubble plot of privacy solutions with the X-axis representing the years and the Y-axis representing data privacy solutions. The result in Fig 11 determines that the amount of research is increasing towards the encryption and the authentication data privacy solutions. On the other hand, research into trust data privacy solutions is abating.

https://doi.org/10.1371/journal.pone.0234312.g011

6.4. RQ4: What are the metrics and measures that are used to assess the current solutions of privacy and data protection in MCC?

As shown in Table 8 , we divided the answer into two parts as follows:

• The first part of Table 8 presents the resources usage metrics, where we found that the highest utilized metric is time consumption, which is represented in 32 studies, followed by communication overhead in 26 studies. The results display that energy consumption, memory consumption on mobile devices, and turnaround-time resources usage metrics received the least attention in the selected primary studies.
• The second part of Table 8 shows the contained solution robustness metrics. The results show two studies for each of the effective recommendation rate, accuracy, authentication request, and authentication response. Also, the results show one study for each of the data randomization, a malicious node detection and management performance (MDP), the addition of new users, operations required, authorities, and privacy and reliability factors.

https://doi.org/10.1371/journal.pone.0234312.t008

As illustrated in Fig 12 , the time consumption is the most used metric resulted in 43%. Followed by communication overhead metrics with 35%. Finally, energy consumption, memory consumption, and turnaround time are presented in 15%, 4%, and 3%, respectively.

https://doi.org/10.1371/journal.pone.0234312.g012

As expounded in Fig 13 , the effective recommendation rate, accuracy, authentication response, and authentication request are the most used metrics with 15%, 15%, 14%, and 14%, respectively. One the other hand, the result shows that most of the solution robustness metrics were employed in less than 8% of the selected primary studies.

https://doi.org/10.1371/journal.pone.0234312.g013

For recognizing metrics and measures trends in MCC, we present the trends in a bubble plot in Fig 14 , the X-axis represents the years, and the Y-axis represents metrics and measures. The outcome indicates that the amount of research in the selected primary studies is increasing towards time consumption, overhead communication, and energy consumption metrics. On the other hand, the number of studies in memory consumption and turnaround time is receiving less attention.

https://doi.org/10.1371/journal.pone.0234312.g014

6.5. RQ 5: What research type facets and contribution type facets are used in MCC?

To answer the first part of this question, we studied the proportion of papers by research type, as shown in Table 9 and Fig 15 . Our studies found the solution proposals are the most published studies with 31 papers (42%), followed by the evaluation research with 23 papers (31%). In contrast, there are 11 philosophical papers (14%), five validation research (6%), and four opinion papers (5%).

https://doi.org/10.1371/journal.pone.0234312.g015

https://doi.org/10.1371/journal.pone.0234312.t009

To answer the second part of the question, we studied the proportion of papers by research type, as shown in Table 9 and Fig 16 . Our studies found the most popular contribution type is the model with 33 papers (45%), followed by the method with 22 papers (30%). In contrast, there are only ten system contributions (13%), and nine Formal studies (12%).

https://doi.org/10.1371/journal.pone.0234312.g016

To discover the research type facets in MCC trends, we illustrate the trends in a bubble plot ( Fig 17 ), the X-axis represents the years, and the Y-axis represents the research type. As demonstrated in Fig 17 , the amount of research in the selected primary studies is increasing towards the solution proposal and the evaluation research. On the other hand, the number of validation research and opinion paper research type facets are decreasing.

https://doi.org/10.1371/journal.pone.0234312.g017

To discover the contribution type facets in MCC, we illustrate the trends in a bubble plot ( Fig 18 ), the X-axis represents the years, and the Y-axis represents the contribution type. The outcome shows that the models and the methods are relatively dominant in the field, and the systems and the formal studies are losing momentum in the domain.

https://doi.org/10.1371/journal.pone.0234312.g018

6.6. RQ 6: What are the currently open research issues of privacy and data protection in MCC?

In this study, we have identified nine main open research issues with 23 examples of future research directions suggested by the authors in privacy and data protection in MCC. Table 10 shows the identified open research issues in the selected primary studies.

https://doi.org/10.1371/journal.pone.0234312.t010

Furthermore, Fig 19 displays the open research issues in privacy and data protection based on the number of studies. As illustrated in Fig 19 , security, authentication, privacy, and encryption were getting momentum in 31%, 13%, 13%, and 13%, respectively. On the other hand, energy consumption, trust, various attacks, architectures, and testing addressed in less than 10% of the selected primary studies for each of them.

https://doi.org/10.1371/journal.pone.0234312.g019

7. Key findings

In this study, a systematic mapping study was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a set of 74 primary studies were selected. In this section, we summaries the key findings of this study as follows:

• The current data privacy exercise in MCC : This study shows that the exercises of cryptography, authentication, account creation, and verification were getting significant attention in 93% of the selected primary studies. In contrast, access control, steganography, and reputation with less attention in less than 8% of the selected primary studies. Also, our results show that the amount of research is increasing in the setup, cryptography, authentication, and accounts creation. Conversely, the outcome shows that the research in verification, access control, steganography, and reputation are losing momentum.
• The data privacy threats and attacks in MCC : The results of this SMS show that the issues of unauthorized, data privacy, leakage of user privacy, data misuse, and untrusted service provider were receiving the most consideration in 68% of the selected primary studies. On the other hand, internal attacks, improper security policies and practices in some locations, internal multi-layer attacks, inference attacks on user privacy, and data breach threats were received less consideration with fewer than 6% of the selected primary studies. Also, our results show that unauthorized, data privacy, leakage of user privacy, and phishing attacks are relatively dominant. Conversely, the outcome indicates that the research in eavesdropping attacks, internal attacks, improper security policies and practices in some locations, internal multi-layer attacks, inference attacks on user privacy, and data breach threats have the lowest studies in the domain.
• The privacy solutions proposed to serve personal data protection in MCC : The results of this SMS show that the encryption, authentication, and access control of the solutions in MCC were getting the highest attention in 97% of the selected primary studies. Trust solutions had the lowest concern in the field with less than 4%. Furthermore, the amount of research is increasing in encryption and the authentication of data privacy solutions in MCC. Contrary to expectations, the outcome shows that the research in the trust solutions in MCC is less likely than expected with only 3% of the selected primary studies.
• ➢. It is interesting to note that this study identified five resources usage metrics and ten solution robustness metrics. In resource usage metrics, around 78% of primary studies assess the time consumption and the communication overhead. In solution robustness metrics, an effective recommendation rate and accuracy were gotten 30% of primary studies.
• ➢. In resource usage metrics, the amount of research is increasing in time consumption and communication overhead metrics and measures. In contrast, energy consumption, memory consumption, and turnaround time are utilized in less than 23% of the selected papers. Furthermore, less than 4% of the primary studies used turnaround time metrics, which indicated that the turnaround time measures are less popular in the domain.
• ➢. In solution robustness metrics, the recognized data randomization, a malicious node detection and the management performance, the addition of new users, operations required, authorities, privacy and reliability factors, authentication requests, and authentication, were gotten less than 8% of the selected papers. On the other hand, the amount of research is increasing in accuracy and effective recommendation of metrics and measures in MCC. Conversely, the outcome shows that the research on privacy and reliability is not dominant in the area.
• The research type facets in MCC: The results show that the solution proposals and evaluation research got considerable attention in 73% of the selected primary studies. The validation research and the opinion papers with the lowest examinations with less than 13% of the selected primary studies. The amount of research is increasing in the solution proposal and evaluation research type. Conversely, the outcome shows that the research in the validation research and opinion paper is losing momentum.
• The Contribution type facets in MCC: The results of this SMS show that the models and the methods got the highest attention in 75% of the selected primary studies. Also, systems and formal studies had gotten the lowest studies in the field with less than 26% of the selected primary studies. In addition, our results show that the amount of research is increasing in the models and the methods of the contribution type facets. Surprisingly, the research in the systems and formal studies are decreased in the selected primary studies.
• Open research issues: In this study, we identified the new challenges in privacy and data protection in MCC, which were noted by the researchers in the selected primary studies. As presented in the previous SMS [ 95 ], the issues that emerged ten years ago are still considered open issues [ 95 ]. Our exploration shows that there are open research issues in encryption, authentication, security, trust, signature-based privacy, architectures, various attacks, testing, and energy consumption. In this SMS, as illustrated in Table 10 , 23 examples of future research directions suggested by the authors are useful for research activities in the future.

8. Threats to validity

The process of SMS is not infallible as with any secondary research method. There are many risks to consider for ensuring the validity of this SMS study. In this part, we describe and relieve the risks to the validity of this study to mitigate the potential risks. The risks include the search criteria, digital databases, and inclusion and exclusion criteria [ 96 ].

8.1. Search criteria

In this examination, the highest attention paid for choosing the most useful search strings. In particular, the construction of the search string is a threat to the validity of this study [ 96 ]. To mitigate this threat, our search string is derived based on PICO criteria [ 13 ]. PICO criteria are popular and widely used in the SMS, and this would enable us to retrieve the wanted studies in the search result and mitigate the threat.

8.2. Digital databases

For this study, the selection of databases, including IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, and Scopus is a threat to the validity of the study since related studies would not be included in those databases. To mitigate this threat, as presented in Kitchenham et al. [ 97 ], and pointed out by Dyba et al. [ 98 ], the selection of IEEE, ACM, and any two databases are enough to save time and effort for general rather than searching multiple publishers’ digital databases [ 97 , 98 ]. Accordingly, in this examination, we selected five databases, including IEEE and ACM, which will mitigate the threat.

8.3. Inclusion and exclusion criteria

In this exploration, the rules and conditions of our inclusion and exclusion criteria are defined to be ranged with the scope of the study. The criteria stemmed from discussions within the research team. However, producing rules to recognize the initial literature to review; means that there is a threat that relevant research may be ignored if it employs various terms to that of the criteria. However, primary search terms of the study’s, namely Privacy, data protection in mobile cloud computing (MCC), are traditional, well-defined and accepted terms, which should decrease the number of ignored studies. Moreover, as the study is focused on identifying the main research in privacy and data protection in the mobile cloud computing, there is not as much of a concern with capturing research that is loosely related to the domain.

9. Conclusion

Mobile cloud computing (MCC) is a significant area of research emerging out of mobile devices and cloud computing [ 3 ]. In recent years, a significant number of studies have been published with a growing interest in privacy and data protection. Along with this advance in MCC, however, no specific research identified the current trends and open issues in privacy and data protection in MCC. This study highlighted current trends and open issues in privacy and data protection in MCC using the results of existing primary studies published from 2009 to 2019.

In this study, a systematic mapping study (SMS) was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a set of 74 primary studies were selected. As a result, the existing threats and attacks on data privacy and solutions to serve personal data were demonstrated. Also, the metrics and measures that are used to assess the current solutions for privacy in mobile cloud computing were aggregated. In addition, the current state-of-the-art of data privacy exercises used in the domain was identified. Moreover, the research type’s facets and the contribution type facets that are used in MCC were highlighted. Furthermore, the open research issues of privacy and data protection in MCC were demonstrated.

This result of this study shows that, for the current data privacy exercise in MCC, the number of investigations is increasing regarding the setup, cryptography, authentication, and accounts creation of data privacy exercise. Also, for data privacy threats and attacks in MCC, the results of this study show the need for research in eavesdropping attacks, internal attacks, improper security policies and practices in some locations, internal multi-layer attacks, inference attacks on user privacy, and data breach threats. In addition, our exploration shows that there are open research issues in encryption, authentication, security, trust, privacy, architectures, various attacks, energy consumption, and testing. Overall, this SMS highlighted the current state-of-the-art, and demonstrated open research issues which in turn allows us to understand the required research into privacy and data protection in MCC.

Finally, this study provides for researchers and practitioners the current state of research in the privacy and data protection in MCC, to help in implementing privacy and data protection in their applications or their investigations. In future work, we plan to conduct a survey to assess possible solutions for preserving privacy and protection in MCC.

Supporting information

S1 appendix. the contribution type facets definitions for systematic mapping study (sms) [ 20 ]..

https://doi.org/10.1371/journal.pone.0234312.s001

S1 Checklist. PRISMA 2009 checklist (Adapted for KIN 4400).

https://doi.org/10.1371/journal.pone.0234312.s002

S1 Fig. PRISMA 20009 flow diagram.

https://doi.org/10.1371/journal.pone.0234312.s003

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Landing cutting-edge jobs becomes reality with new MSU computing technology degrees

Contact: Harriet Laird

STARKVILLE, Miss.—The future is bright for those interested in cutting-edge jobs in computing technologies, and Mississippi State is offering three new degree paths this fall to get students on their way to professional success.

MSU’s new Bachelor of Applied Science in Cybersecurity, Bachelor of Science in Artificial Intelligence and Master of Applied Data Science offer students hands-on training in occupations growing much faster for the next 12 years than the average for all jobs.

Currently, the university has existing programs that include a Bachelor of Cybersecurity, Bachelor of Data Science and a Master of Cybersecurity and Operations.

“As a comprehensive public research university, we want to ensure all our programs teach innovative and current best practices and prepare our students to be career ready when they graduate,” said MSU’s David Shaw, provost and executive vice president. “Every day, we’re looking at the whole picture—analyzing employers’ needs and workforce demands to offer top-notch academic programs.”

The U.S. Bureau of Labor Statistics projects about 377,500 openings through 2032, on average, in computer and information technology occupations. While many openings are due to employment growth, an aging and retiring workforce also is a contributor. The bureau reports the median annual wage at $104,420 as of 2023, significantly higher than the median annual wage of $48,060 for all occupations.

Also this fall, MSU is introducing three additional applied science bachelor’s degrees—business office technology, healthcare administration and public management. Applied science bachelor’s degree programs are ideal for those who have completed or are finishing a two-year college or military Associate of Applied Science degree.

Offering online, in-person and hybrid learning options, these degrees add flexibility for working adults and transfer students who have A.A.S. technical degrees in a variety of fields. Students in most of these programs will have the opportunity to gain credentials of value while earning their degree.

A graduate degree for teachers is a new addition as well. The Master of Arts in Teaching Elementary Level Alternate Route is specifically designed to address the state’s challenge of recruiting and retaining quality teachers. More than 100 public school districts have critical teacher shortages.

New Degree Programs: A Closer Look

—The B.A.S. in Cybersecurity is designed to equip students with the knowledge, skills and expertise to become cybersecurity analysts. This program ensures graduates are well-versed in cybersecurity theoretical aspects and possess hands-on skills required in defending organizations against cyber threats.

—Students pursuing the B.S. in Artificial Intelligence gain core theoretical knowledge and skills training to design and develop artificial intelligence systems. Data analytics, machine learning, robotics and more are the foundation of a degree preparing graduates for careers such as AI researcher and data scientist in technology and healthcare and many other industries.

—MSU is offering the Master of Applied Data Science focused toward working adults who may have a variety of bachelor’s degrees. While students learn foundational data science concepts, they also gain practical skills using real world datasets in many application domains. Careers for data scientists are innumerable—from agriculture and athletics to finance and healthcare.

—The B.A.S. in Business Office Technology prepares those holding business/technology A.A.S. degrees for work as office managers, administrative supervisors, IT administrators and more. It is delivered in both online and face-to-face formats, accommodating the preferences and schedules of a diverse population interested in pursuing a BOT degree.

—Those with health-related A.A.S. degrees can pursue MSU’s B.A.S. in Healthcare Administration at MSU-Meridian, where students are prepared to become managers in such settings as hospitals, private practices, pharmaceutical agencies, insurance companies and more. The curriculum includes study and skills training in such subjects as healthcare finance, law and management.

—MSU’s new B.A.S. in Public Management is specifically to advance the education and career options of professionals with existing A.A.S. degrees in public safety areas such as fire science, law enforcement or emergency medical/management services. Jobs for graduates include city or emergency manager, criminal justice administrator, fire management officer and others.

—A new graduate-level licensure program, the M.A. in Teaching Elementary Level Alternate Route prepares students for highly competent instruction in kindergarten through sixth grade. The MSU-Meridian coursework includes planning and managing learning, assessment and serving children with special needs. A one-year residency includes diagnosing reading problems and more.

Mississippi State University is taking care of what matters. Learn more at www.msstate.edu .

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What Is Artificial Intelligence? Definition, Uses, and Types

Learn what artificial intelligence actually is, how it’s used today, and what it may do in the future.

Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems. 

Today, the term “AI” describes a wide range of technologies that power many of the services and goods we use every day – from apps that recommend tv shows to chatbots that provide customer support in real time. But do all of these really constitute artificial intelligence as most of us envision it? And if not, then why do we use the term so often? 

In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further.  

Want to try out your AI skills? Enroll in AI for Everyone, an online program offered by DeepLearning.AI. In just 6 hours , you'll gain foundational knowledge about AI terminology , strategy , and the workflow of machine learning projects . Your first week is free .

What is artificial intelligence?

Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses a wide variety of technologies, including machine learning , deep learning , and natural language processing (NLP) . 

Although the term is commonly used to describe a range of different technologies in use today, many disagree on whether these actually constitute artificial intelligence. Instead, some argue that much of the technology used in the real world today actually constitutes highly advanced machine learning that is simply a first step towards true artificial intelligence, or “general artificial intelligence” (GAI).

Yet, despite the many philosophical disagreements over whether “true” intelligent machines actually exist, when most people use the term AI today, they’re referring to a suite of machine learning-powered technologies, such as Chat GPT or computer vision, that enable machines to perform tasks that previously only humans can do like generating written content, steering a car, or analyzing data. 

Artificial intelligence examples 

Though the humanoid robots often associated with AI (think Star Trek: The Next Generation’s Data or Terminator’s   T-800) don’t exist yet, you’ve likely interacted with machine learning-powered services or devices many times before. 

At the simplest level, machine learning uses algorithms trained on data sets to create machine learning models that allow computer systems to perform tasks like making song recommendations, identifying the fastest way to travel to a destination, or translating text from one language to another. Some of the most common examples of AI in use today include: 

ChatGPT : Uses large language models (LLMs) to generate text in response to questions or comments posed to it. 

Google Translate: Uses deep learning algorithms to translate text from one language to another. 

Netflix: Uses machine learning algorithms to create personalized recommendation engines for users based on their previous viewing history. 

Tesla: Uses computer vision to power self-driving features on their cars. 

Read more: Deep Learning vs. Machine Learning: Beginner’s Guide

The increasing accessibility of generative AI tools has made it an in-demand skill for many tech roles . If you're interested in learning to work with AI for your career, you might consider a free, beginner-friendly online program like Google's Introduction to Generative AI .

AI in the workforce

Artificial intelligence is prevalent across many industries. Automating tasks that don't require human intervention saves money and time, and can reduce the risk of human error. Here are a couple of ways AI could be employed in different industries:

Finance industry. Fraud detection is a notable use case for AI in the finance industry. AI's capability to analyze large amounts of data enables it to detect anomalies or patterns that signal fraudulent behavior.

Health care industry. AI-powered robotics could support surgeries close to highly delicate organs or tissue to mitigate blood loss or risk of infection.

Not ready to take classes or jump into a project yet? Consider subscribing to our weekly newsletter, Career Chat . It's a low-commitment way to stay current with industry trends and skills you can use to guide your career path.

What is artificial general intelligence (AGI)? 

Artificial general intelligence (AGI) refers to a theoretical state in which computer systems will be able to achieve or exceed human intelligence. In other words, AGI is “true” artificial intelligence as depicted in countless science fiction novels, television shows, movies, and comics. 

As for the precise meaning of “AI” itself, researchers don’t quite agree on how we would recognize “true” artificial general intelligence when it appears. However, the most famous approach to identifying whether a machine is intelligent or not is known as the Turing Test or Imitation Game, an experiment that was first outlined by influential mathematician, computer scientist, and cryptanalyst Alan Turing in a 1950 paper on computer intelligence. There, Turing described a three-player game in which a human “interrogator” is asked to communicate via text with another human and a machine and judge who composed each response. If the interrogator cannot reliably identify the human, then Turing says the machine can be said to be intelligent [ 1 ]. 

To complicate matters, researchers and philosophers also can’t quite agree whether we’re beginning to achieve AGI, if it’s still far off, or just totally impossible. For example, while a recent paper from Microsoft Research and OpenAI argues that Chat GPT-4 is an early form of AGI, many other researchers are skeptical of these claims and argue that they were just made for publicity [ 2 , 3 ].

Regardless of how far we are from achieving AGI, you can assume that when someone uses the term artificial general intelligence, they’re referring to the kind of sentient computer programs and machines that are commonly found in popular science fiction. 

Strong AI vs. Weak AI

When researching artificial intelligence, you might have come across the terms “strong” and “weak” AI. Though these terms might seem confusing, you likely already have a sense of what they mean. 

Strong AI is essentially AI that is capable of human-level, general intelligence. In other words, it’s just another way to say “artificial general intelligence.” 

Weak AI , meanwhile, refers to the narrow use of widely available AI technology, like machine learning or deep learning, to perform very specific tasks, such as playing chess, recommending songs, or steering cars. Also known as Artificial Narrow Intelligence (ANI), weak AI is essentially the kind of AI we use daily.

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

The 4 Types of AI 

As researchers attempt to build more advanced forms of artificial intelligence, they must also begin to formulate more nuanced understandings of what intelligence or even consciousness precisely mean. In their attempt to clarify these concepts, researchers have outlined four types of artificial intelligence .

Here’s a summary of each AI type, according to Professor Arend Hintze of the University of Michigan [ 4 ]: 

1. Reactive machines

Reactive machines are the most basic type of artificial intelligence. Machines built in this way don’t possess any knowledge of previous events but instead only “react” to what is before them in a given moment. As a result, they can only perform certain advanced tasks within a very narrow scope, such as playing chess, and are incapable of performing tasks outside of their limited context. 

2. Limited memory machines

Machines with limited memory possess a limited understanding of past events. They can interact more with the world around them than reactive machines can. For example, self-driving cars use a form of limited memory to make turns, observe approaching vehicles, and adjust their speed. However, machines with only limited memory cannot form a complete understanding of the world because their recall of past events is limited and only used in a narrow band of time. 

3. Theory of mind machines

Machines that possess a “theory of mind” represent an early form of artificial general intelligence. In addition to being able to create representations of the world, machines of this type would also have an understanding of other entities that exist within the world. As of this moment, this reality has still not materialized. 

4. Self-aware machines

Machines with self-awareness are the theoretically most advanced type of AI and would possess an understanding of the world, others, and itself. This is what most people mean when they talk about achieving AGI. Currently, this is a far-off reality. 

AI benefits and dangers

AI has a range of applications with the potential to transform how we work and our daily lives. While many of these transformations are exciting, like self-driving cars, virtual assistants, or wearable devices in the healthcare industry, they also pose many challenges.

It’s a complicated picture that often summons competing images: a utopia for some, a dystopia for others. The reality is likely to be much more complex. Here are a few of the possible benefits and dangers AI may pose: 

These are just some of the ways that AI provides benefits and dangers to society. When using new technologies like AI, it’s best to keep a clear mind about what it is and isn’t. With great power comes great responsibility, after all. 

Read more: AI Ethics: What It Is and Why It Matters

Build AI skills on Coursera

Artificial Intelligence is quickly changing the world we live in. If you’re interested in learning more about AI and how you can use it at work or in your own life, consider taking a relevant course on Coursera today. 

In DeepLearning.AI’s AI For Everyone course , you’ll learn what AI can realistically do and not do, how to spot opportunities to apply AI to problems in your own organization, and what it feels like to build machine learning and data science projects. 

In DeepLearning.AI’s AI For Good Specialization , meanwhile, you’ll build skills combining human and machine intelligence for positive real-world impact using AI in a beginner-friendly, three-course program. 

Article sources

UMBC. “ Computing Machinery and Intelligence by A. M. Turing , https://redirect.cs.umbc.edu/courses/471/papers/turing.pdf.” Accessed March 30, 2024.

ArXiv. “ Sparks of Artificial General Intelligence: Early experiments with GPT-4 , https://arxiv.org/abs/2303.12712.” Accessed March 30, 2024.

Wired. “ What’s AGI, and Why Are AI Experts Skeptical? , https://www.wired.com/story/what-is-artificial-general-intelligence-agi-explained/.” Accessed March 30, 2024.

GovTech. “ Understanding the Four Types of Artificial Intelligence , https://www.govtech.com/computing/understanding-the-four-types-of-artificial-intelligence.html.” Accessed March 30, 2024.

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McKinsey Technology Trends Outlook 2023

After a tumultuous 2022 for technology investment and talent, the first half of 2023 has seen a resurgence of enthusiasm about technology’s potential to catalyze progress in business and society. Generative AI deserves much of the credit for ushering in this revival, but it stands as just one of many advances on the horizon that could drive sustainable, inclusive growth and solve complex global challenges.

To help executives track the latest developments, the McKinsey Technology Council  has once again identified and interpreted the most significant technology trends unfolding today. While many trends are in the early stages of adoption and scale, executives can use this research to plan ahead by developing an understanding of potential use cases and pinpointing the critical skills needed as they hire or upskill talent to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, and investment to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into underlying technologies, uncertainties, and questions surrounding each trend. This year, we added an important new dimension for analysis—talent. We provide data on talent supply-and-demand dynamics for the roles of most relevance to each trend. (For more, please see the sidebar, “Research methodology.”)

New and notable

All of last year’s 14 trends remain on our list, though some experienced accelerating momentum and investment, while others saw a downshift. One new trend, generative AI, made a loud entrance and has already shown potential for transformative business impact.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend. Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents.
• Research. Data on research publications are sourced from the Lens (www.lens.org).
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data is drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s study to reflect the evolution of technology trends:

• The generative-AI trend was added since last year’s study.
• We adjusted the definitions of electrification and renewables (previously called future of clean energy) and climate technologies beyond electrification and renewables (previously called future of sustainable consumption).
• Data sources were updated. This year, we included only closed deals in PitchBook data, which revised downward the investment numbers for 2018–22. For future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

This new entrant represents the next frontier of AI. Building upon existing technologies such as applied AI and industrializing machine learning, generative AI has high potential and applicability across most industries. Interest in the topic (as gauged by news and internet searches) increased threefold from 2021 to 2022. As we recently wrote, generative AI and other foundational models  change the AI game by taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users. Generative AI is poised to add as much as $4.4 trillion in economic value from a combination of specific use cases and more diffuse uses—such as assisting with email drafts—that increase productivity. Still, while generative AI can unlock significant value, firms should not underestimate the economic significance and the growth potential that underlying AI technologies and industrializing machine learning can bring to various industries.

Investment in most tech trends tightened year over year, but the potential for future growth remains high, as further indicated by the recent rebound in tech valuations. Indeed, absolute investments remained strong in 2022, at more than $1 trillion combined, indicating great faith in the value potential of these trends. Trust architectures and digital identity grew the most out of last year’s 14 trends, increasing by nearly 50 percent as security, privacy, and resilience become increasingly critical across industries. Investment in other trends—such as applied AI, advanced connectivity, and cloud and edge computing—declined, but that is likely due, at least in part, to their maturity. More mature technologies can be more sensitive to short-term budget dynamics than more nascent technologies with longer investment time horizons, such as climate and mobility technologies. Also, as some technologies become more profitable, they can often scale further with lower marginal investment. Given that these technologies have applications in most industries, we have little doubt that mainstream adoption will continue to grow.

Organizations shouldn’t focus too heavily on the trends that are garnering the most attention. By focusing on only the most hyped trends, they may miss out on the significant value potential of other technologies and hinder the chance for purposeful capability building. Instead, companies seeking longer-term growth should focus on a portfolio-oriented investment across the tech trends most important to their business. Technologies such as cloud and edge computing and the future of bioengineering have shown steady increases in innovation and continue to have expanded use cases across industries. In fact, more than 400 edge use cases across various industries have been identified, and edge computing is projected to win double-digit growth globally over the next five years. Additionally, nascent technologies, such as quantum, continue to evolve and show significant potential for value creation. Our updated analysis for 2023 shows that the four industries likely to see the earliest economic impact from quantum computing—automotive, chemicals, financial services, and life sciences—stand to potentially gain up to $1.3 trillion in value by 2035. By carefully assessing the evolving landscape and considering a balanced approach, businesses can capitalize on both established and emerging technologies to propel innovation and achieve sustainable growth.

Tech talent dynamics

We can’t overstate the importance of talent as a key source in developing a competitive edge. A lack of talent is a top issue constraining growth. There’s a wide gap between the demand for people with the skills needed to capture value from the tech trends and available talent: our survey of 3.5 million job postings in these tech trends found that many of the skills in greatest demand have less than half as many qualified practitioners per posting as the global average. Companies should be on top of the talent market, ready to respond to notable shifts and to deliver a strong value proposition to the technologists they hope to hire and retain. For instance, recent layoffs in the tech sector may present a silver lining for other industries that have struggled to win the attention of attractive candidates and retain senior tech talent. In addition, some of these technologies will accelerate the pace of workforce transformation. In the coming decade, 20 to 30 percent of the time that workers spend on the job could be transformed by automation technologies, leading to significant shifts in the skills required to be successful. And companies should continue to look at how they can adjust roles or upskill individuals to meet their tailored job requirements. Job postings in fields related to tech trends grew at a very healthy 15 percent between 2021 and 2022, even though global job postings overall decreased by 13 percent. Applied AI and next-generation software development together posted nearly one million jobs between 2018 and 2022. Next-generation software development saw the most significant growth in number of jobs (exhibit).

Image description:

Small multiples of 15 slope charts show the number of job postings in different fields related to tech trends from 2021 to 2022. Overall growth of all fields combined was about 400,000 jobs, with applied AI having the most job postings in 2022 and experiencing a 6% increase from 2021. Next-generation software development had the second-highest number of job postings in 2022 and had 29% growth from 2021. Other categories shown, from most job postings to least in 2022, are as follows: cloud and edge computing, trust architecture and digital identity, future of mobility, electrification and renewables, climate tech beyond electrification and renewables, advanced connectivity, immersive-reality technologies, industrializing machine learning, Web3, future of bioengineering, future of space technologies, generative AI, and quantum technologies.

End of image description.

This bright outlook for practitioners in most fields highlights the challenge facing employers who are struggling to find enough talent to keep up with their demands. The shortage of qualified talent has been a persistent limiting factor in the growth of many high-tech fields, including AI, quantum technologies, space technologies, and electrification and renewables. The talent crunch is particularly pronounced for trends such as cloud computing and industrializing machine learning, which are required across most industries. It’s also a major challenge in areas that employ highly specialized professionals, such as the future of mobility and quantum computing (see interactive).

Michael Chui is a McKinsey Global Institute partner in McKinsey’s Bay Area office, where Mena Issler is an associate partner, Roger Roberts  is a partner, and Lareina Yee  is a senior partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Bharat Bahl, Soumya Banerjee, Arjita Bhan, Tanmay Bhatnagar, Jim Boehm, Andreas Breiter, Tom Brennan, Ryan Brukardt, Kevin Buehler, Zina Cole, Santiago Comella-Dorda, Brian Constantine, Daniela Cuneo, Wendy Cyffka, Chris Daehnick, Ian De Bode, Andrea Del Miglio, Jonathan DePrizio, Ivan Dyakonov, Torgyn Erland, Robin Giesbrecht, Carlo Giovine, Liz Grennan, Ferry Grijpink, Harsh Gupta, Martin Harrysson, David Harvey, Kersten Heineke, Matt Higginson, Alharith Hussin, Tore Johnston, Philipp Kampshoff, Hamza Khan, Nayur Khan, Naomi Kim, Jesse Klempner, Kelly Kochanski, Matej Macak, Stephanie Madner, Aishwarya Mohapatra, Timo Möller, Matt Mrozek, Evan Nazareth, Peter Noteboom, Anna Orthofer, Katherine Ottenbreit, Eric Parsonnet, Mark Patel, Bruce Philp, Fabian Queder, Robin Riedel, Tanya Rodchenko, Lucy Shenton, Henning Soller, Naveen Srikakulam, Shivam Srivastava, Bhargs Srivathsan, Erika Stanzl, Brooke Stokes, Malin Strandell-Jansson, Daniel Wallance, Allen Weinberg, Olivia White, Martin Wrulich, Perez Yeptho, Matija Zesko, Felix Ziegler, and Delphine Zurkiya.

They also wish to thank the external members of the McKinsey Technology Council.

This interactive was designed, developed, and edited by McKinsey Global Publishing’s Nayomi Chibana, Victor Cuevas, Richard Johnson, Stephanie Jones, Stephen Landau, LaShon Malone, Kanika Punwani, Katie Shearer, Rick Tetzeli, Sneha Vats, and Jessica Wang.

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Power Tool: Generative AI Tracks Typhoons, Tames Energy Use

Weather forecasters in Taiwan had their hair blown back when they saw a typhoon up close, created on a computer that slashed the time and energy needed for the job.

It’s a reaction that users in many fields are feeling as generative AI shows them how new levels of performance contribute to reductions in total cost of ownership.

Inside the AI of the Storm

Tracking a typhoon provided a great test case of generative AI’s prowess. The work traditionally begins with clusters of CPUs cranking on complex algorithms to create atmospheric models with a 25-kilometer resolution.

Enter CorrDiff , a generative AI model that’s part of NVIDIA Earth-2 , a set of services and software for weather and climate research.

Using a class of diffusion models that power today’s text-to-image services, CorrDiff resolved the 25-km models to two kilometers 1,000x faster, using 3 , 000x less energy for a single inference than traditional methods.

CorrDiff Cuts Costs 50x, Energy Use 25x

CorrDiff shines on the NVIDIA AI platform, even when retraining the model once a year and using statistical groups of a thousand forecasts to boost the accuracy of predictions. Compared to traditional methods under these conditions, it slashes cost by 50x cost and energy use by 25x a year.

That means work that used to require nearly $3 million for a cluster of CPUs and the energy to run them can be done for about $60,000 on a single system with an NVIDIA H100 Tensor Core GPU . It’s a massive reduction that shows how generative AI and accelerated computing increases energy efficiency and lowers total cost of ownership.

The technology also helps forecasters see more precisely where a typhoon will land, potentially saving lives.

“NVIDIA’s CorrDiff generative AI model opens the door to the use of AI-generated kilometer-scale weather forecasts, enabling Taiwan to prepare better for typhoons,” said Hongey Chen, a director of Taiwan’s National Science and Technology Center for Disaster Reduction.

The Taiwan forecasters could save nearly a gigawatt-hour a year, using CorrDiff. Energy savings could balloon if the nearly 200 regional weather data centers around the world adopt the technology for more sustainable computing .

Companies that sell commercial forecasts are also adopting CorrDiff , attracted by its speed and savings.

Broad Horizons for Energy Efficiency

NVIDIA Earth-2 takes these capabilities to a planetary scale. It fuses AI, physics simulations and observed data to help countries and companies respond to global issues like climate change. That will help address the impacts of climate change, which is expected to cost a million lives and $1.7 trillion per year by 2050.

Accelerated computing and generative AI are bringing new levels of performance and energy efficiency to many applications. Explainers on green computing and why GPUs are great for AI provide more context and some examples.

Compare the costs and energy consumption of popular workloads running on an x86 CPU-based server versus an NVIDIA GPU server with this simple calculator . And watch Huang’s keynote address at COMPUTEX to get the big picture.

NVIDIA websites use cookies to deliver and improve the website experience. See our cookie policy for further details on how we use cookies and how to change your cookie settings.

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Mobile Computing

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To ground my work on the design of mobile interactions I will first briefly trace the history of mobile computing. The purpose of this is to map out the origins of this field of research and design, show how it is continually evolving, and illustrate the influence of careful and innovative mobile interaction design at different points in time. This is followed by an introduction to the discipline of interaction design and its established design approaches.

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