- Email: [email protected]
A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science (2018) 000–000 Procedia Computer Science 12900 (2018) 368–371
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
2017 International Conference on Identification, Information and Knowledge in the Internet of 2017 International Conference on Identification, Information and Knowledge in the Internet of Things Things
A A Location Location Privacy Privacy Preserving Preserving Scheme Scheme Based Based on on Repartitioning Repartitioning Anonymous Region in Mobile Social Network Anonymous Region in Mobile Social Network Lina Nia,b , Yanfeng Yuana , Xiao Wanga , Mengmeng Zhanga , Jinquan Zhanga,∗ Linaa Nia,b , Yanfeng Yuana , Xiao Wanga , Mengmeng Zhanga , Jinquan Zhanga,∗ College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
a College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China b The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China b The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China
Abstract Abstract Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services Applying the users proliferated location-based services (LBS) to social spawned network (MSN) services that it allows to discover potential friends around them. In thisnetworks paper, wehas focus on the mobile problemsocial of location privacy preserving that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via the to repartition regionthe where the central anonymous location minimizes the traffic between the anonymous server and LBS serveranonymous while protecting privacy central anonymous of the user location.location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. c 2018 Copyright 2018 Elsevier Elsevier Ltd. Ltd. All All rights rights reserved. reserved. Copyright © c 2018 Copyright Elsevierunder Ltd. All rights reserved. Selection and and peer-review Selection peer-review under responsibility responsibility of of the the scientific scientific committee committee of of the the 2017 2017 International International Conference Conference on on Identification, Identification, Selection andand peer-review under responsibility of the scientific committee of the 2017 International Conference on Identification, Information Information and Knowledge Knowledge in in the the Internet Internet of of Things Things (IIKI2017). (IIKI2017). Information and Knowledge in the Internet of Things (IIKI2017). Keywords: Keywords: Privacy preserving; mobile social network; location-based service; location privacy; anonymous region repartition. Privacy preserving; mobile social network; location-based service; location privacy; anonymous region repartition.
1. Introduction 1. Introduction Internet of Things (IoT), a trend of future networks, is immersing into many aspects of our personal and working of Thingsmore (IoT), a trend of future networks, is immersing into many aspects personal and working life,Internet it also provides comprehensive intelligent service. Social networks used widelyofinour mobile Internet catalyze life, it also provides more comprehensive intelligent service. Social networks used widely in mobile Internet catalyze mobile social networks (MSN), and users in MSN can not only acquire their own location information and sign in a mobile social networks (MSN), and users in MSN can not only acquire their own location information and sign in a location but also find nearby friends, access to location-based services (LBS) such as finding the nearest hotel, finding location but also find nearby friends, access to location-based services (LBS) such as finding the nearest hotel, finding directions, sharing action tracks, and so on [1, 2, 3, 4, 5, 6]. However, when we enjoy the convenience of LBS and directions, sharing action tracks, andconfront so on [1, 2, 3, 5, 6]. However, when wewhich enjoy isthe convenience LBS and MSN services, the mobile users also with the4,risk of location disclosure, a severe privacyofpreserving MSN services, the mobile users also confront with the risk of location disclosure, which is a severe privacy preserving concern [7, 8, 9, 10]. concern [7, 8,research 9, 10]. on the privacy protection technology based on location-based services (LBS) has attracted conRecently, Recently, research technology based onlocation location-based services (LBS) has attracted siderable interest. [11,on 12,the 13,privacy 14, 15].protection Besides the above described privacy preserving technologies, thereconare siderable interest. [11, 12, 13, 14, 15]. Besides the above described location privacy preserving technologies, there are ∗ ∗
Corresponding author. Tel.: +86-532-86057126 ; fax: +86-532-86057126. Corresponding Tel.: +86-532-86057126 ; fax: +86-532-86057126. E-mail address:author. [email protected], [email protected], [email protected], [email protected], [email protected] E-mail address: [email protected], [email protected], [email protected], [email protected], [email protected] c 2018 Elsevier Ltd. All rights reserved. 1877-0509 Copyright c 2018 1877-0509and Copyright Elsevier Ltd. Allof rights scientific reserved. committee of the 2017 International Conference on Identification, Information and Selection peer-review under responsibility 1877-0509 Copyright © 2018 Elsevier Ltd. Allthe rights reserved. Selection and peer-review responsibility of the scientific committee of the 2017 International Conference on Identification, Information and Knowledge in peer-review the Internet under of Things (IIKI2017). Selection and under responsibility of the scientific committee of the 2017 International Conference on Identification, Information Knowledge in theinInternet of Things (IIKI2017). and Knowledge the Internet of Things (IIKI2017). 10.1016/j.procs.2018.03.091
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Lina Ni et al. / Procedia Computer Science 129 (2018) 368–371 Lina Ni et al. / Procedia Computer Science 00 (2018) 000–000
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a wealth of methods such as location data randomization [16, 17], fuzzification of space or time data [18], methods based on strategies and encryption [19], sensitive semantic based security anonymity mechanism [20, 21] In this paper, we focus on the location privacy preserving in MSN aiming at larger communication overhead, larger range and inaccuracy of query results for traditional anonymous schemes. 2. System Model As shown in Fig. 1, the principles of the model are as follows: 1. When the user requests the location query service, all the query contents, location information and parameters needed to be set are sent to the central anonymous servers. 2. After receiving the query information sent by users, according to certain rules, the central anonymous servers will generate an anonymous user set which meets the requirements, figure out the number of sub-anonymity regions and then partition them, a few scattered sub-anonymity regions are yielded. When the sub-anonymity regions meet the requirements, its central location is computed to replace corresponding sub-anonymity regions to send requests to the LBS server. 3. The LBS server handles the query information sent by the central anonymous servers and returns the query results. 4. After the refinement process, the central anonymous servers return the corresponding results to the users.
Fig. 1. Architecture of our system model.
3. Location Repartitioning Anonymous Region Scheme Combined with Fig. 2 (a) and (b), the basic idea of RPAR scheme is elaborated as follows: 1. The solid red dots represent the users initiating query who find k−1 users with whom the query users can form k-anonymity regions according to the nearest neighbor principle, and k users information set is recorded. It can be seen in Fig. 2 that k=14. 2. According to the parameter n, the number of sub-anonymity regions, the k users are divided into n sub-anonymity regions, so the number of users each sub-anonymity region contains is k =k/n=4. The mobile users (red dots) are as the center to search other nearest k −1 users and form the first sub-anonymity region. 3. A user from the rest of the users which is not in the first sub-anonymity region is randomly selected as the central point. According to the nearest neighbor principle, the sub-anonymity regions are formed with the user and other 3 users who is not the first sub-region form anonymous region, until the rest user number is 0 or below k . 4. The tail anonymity user set is repartitioned into other sub-anonymity regions according to the nearest neighbor principle, and the sub-anonymity regions are updated.
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Lina Ni et al. / Procedia Computer Science 129 (2018) 368–371 Lina Ni et al. / Procedia Computer Science 00 (2018) 000–000
(a) Before partition of k-anonymity region
3
(b) After partition of k-anonymity region
Fig. 2. Repartition of anonymous region.
5. The area of the sub-anonymity regions is calculated. If the total area of the sub-anonymity regions is greater than Amin , users in the maximum sub-anonymity region will be repartitioned until the total area of the sub-anonymity regions is not greater than Amin . 6. The central locations of all the sub-anonymity regions are computed which are used to replace their subanonymity regions to issue queries to the LBS servers. The pseudo-code is given in the Algorithm 1.
Algorithm 1 RPAR Input: uid, (x, y), k, l, n, Amin , M, other users’ location information in anonymous set stored in anonymous servers. Output: central locations (xi , yi ) and M. 1: k =k/n 2: form the first sub-anonymity region subAR1 , calculate its central location (x1 , y1 ) and area S 1 based on (x, y), k and other user location information. 3: for i=1 to n do 4: m=k−k ∗(i−1); 5: select user ui randomly from the rest of the tail anonymity user set um : ui =Random(um ); 6: form subARi ,calculate its central location (xi , yi ) and area S i according to the information of ui and parameter settings; 7: calculate total area of all the sub-anonymity regions: S A =S A +S i ; 8: end for 9: if k mod n== 0 then 10: if S A >Amin then 11: repartition the largest sub-anonymity region until S A
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4. Conclusions and Future Work Aiming at large communication overhead, large range and inaccuracy of query results for traditional anonymous schemes, this paper proposed an anonymous region repartition algorithm by studying the user’s location privacy preservation. The anonymous region is divided into several sub regions, the users’ real locations are replaced by the central location, and a repartition is carried out to solve the remaining users after the anonymous region segmentation. In the future, we will research on the location privacy preserving in the scenario of dense region. Acknowledgment This work is supported by NSF of China under Grant 61672321, 61771289 and 61373027, Training Program of the Major Research Plan of NSF of China under Grant 91746104, National Key R & D Programs Project of China under Grant 2017YFC0804406, Project of Shandong Province Higher Educational Science and Technology Program under Grant J13LN18, J15LN19, Open Project of Tongji University Embedded System and Service Computing of Ministry of Education of China under Grant ESSCKF 2015-02. References [1] T. Song, R. Li, B. Mei et al. (2017) “A privacy preserving communication protocol for IoT applications in smart homes.” IEEE Internet of Things Journal 4 (6): 1844–1852. [2] R. Li, T. Song, N. Capurso et al. (2017) “IoT applications on secure smart shopping system.” IEEE Internet of Things Journal 4 (6): 1945–1954. [3] T. Song, N. Capurso, X. Cheng et al. (2017) “Enhancing GPS with lane-level navigation to facilitate highway driving.” IEEE Transactions on Vehicular Technology 66 (6): 4579–4591. [4] X. Xiao, C. Chen, A. K. Sangaiah et al. (2017) “Cenlocshare: a centralized privacy-preserving location-sharing system for mobile online social networks,” Future Generation Computing Systems, to be published, doi:10.1016/j.future.2017.01.035. [5] H. Shen, G. Bai, M. Yang et al. (2017) “Protecting trajectory privacy: a user-centric analysis.” Journal of Network and Computer Applications 82: 128–139. [6] X. Zheng, G. Luo, and Z. Cai. (2018) “A fair mechanism for private data publication in online social networks.” IEEE Transactions on Network Science and Engineering, to be published, doi:10.1109/TNSE.2018.2679483. [7] Y. Liang, Z. Cai, Q. Han et al. (2017) “Location privacy leakage through sensory data.” Security and Communication Networks 2017 (11):1–12. [8] M. L. Damiani. (2012) “Privacy enhancing techniques for the protection of mobility patterns in LBS: Research issues and trends.” Communications in Computer and Information Science 244: 338–343. [9] C. Hu, W. Li, X. Cheng et al. (2017) “A secure and verifiable access control scheme for big data storage in clouds,” IEEE Transactions on Big Data, to be published, doi:10.1109/TBDATA.2016.2621106. [10] X. Zheng, Z. Cai, J. Yu et al. (2017) “Follow but no track: privacy preserved profile publishing in cyber-physical social systems.” IEEE Internet of Things Journal 4 (6): 1878–1878. [11] A. Sadilek, H. Kautz, and J. P. Bigham. (2012) “Finding your friends and following them to where you are”, on Web Search and Data Mining (WSDM) Proceeding of 5th ACM International Conference, Washington , ACM. [12] A. Ye, S. Lin, J. Ma et al. (2015) “An active diffusion location privacy protection method.” Acta Electronica Sinica 43 (7): 1362–1368. [13] K. Xing, C. Hu, J. Yu et al. (2017) “Mutual privacy preserving K-means clustering in social participatory sensing.” IEEE Transactions on Industrial Informatics 13 (4): 2066–2076. [14] Z. He, Z. Cai, J. Yu et al. (2017) “Cost-efficient strategies for restraining rumor spreading in mobile social networks.” IEEE Transactions on Vehicular Technology 66 (3): 2789–2800. [15] X. Liang, K. Zhang, X. Shen et al. (2014) “Security and privacy in mobile social networks: challenges and solutions.” IEEE Wireless Communications 21 (1): 33–41. [16] G. Sun, Y. Xie, D. Liao et al. (2017) “User-defined privacy location-sharing system in mobile online social networks.” Journal of Network and Computer Applications 86 (C): 34–45. [17] R. Kato, M. Iwata, T. Hara et al. (2012) “A dummy-based anonymization method based on user trajectory with pauses”, in Geographic Information Systems (AGIS) Proceeding of 20th ACM International Conference on Advances, Redondo Beach, ACM. [18] X. Pan, J. Xu, and X. Meng. (2012) “Protecting location privacy against location-dependent attacks in mobile services.” IEEE Transactions on Knowledge and Data Engineering 24 (8): 1506–1519. [19] I. T. Lien, H. s. Lin, Yu, J. R. Shieh et al. (2013) “A novel privacy preserving location-based service protocol with secret circular shift for K-NN search.” IEEE Transactions on Information Forensics and Security 8 (6): 863–873. [20] M. Li, Z. Qin, and C. Wang. (2014) “Sensitive semantics-aware personality cloaking on road-network environment.” International Journal of Security and Its Applications 8 (1): 133–146. [21] L. Zhang, Z. Cai, and X. Wang. (2016) “Fakemask: a novel privacy preserving approach for smartphones.” IEEE Transactions on Network and Service Management 13 (2): 335–348.
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science (2018) 000–000 Procedia Computer Science 12900 (2018) 368–371
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
2017 International Conference on Identification, Information and Knowledge in the Internet of 2017 International Conference on Identification, Information and Knowledge in the Internet of Things Things
A A Location Location Privacy Privacy Preserving Preserving Scheme Scheme Based Based on on Repartitioning Repartitioning Anonymous Region in Mobile Social Network Anonymous Region in Mobile Social Network Lina Nia,b , Yanfeng Yuana , Xiao Wanga , Mengmeng Zhanga , Jinquan Zhanga,∗ Linaa Nia,b , Yanfeng Yuana , Xiao Wanga , Mengmeng Zhanga , Jinquan Zhanga,∗ College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
a College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China b The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China b The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China
Abstract Abstract Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services Applying the users proliferated location-based services (LBS) to social spawned network (MSN) services that it allows to discover potential friends around them. In thisnetworks paper, wehas focus on the mobile problemsocial of location privacy preserving that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via the to repartition regionthe where the central anonymous location minimizes the traffic between the anonymous server and LBS serveranonymous while protecting privacy central anonymous of the user location.location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. c 2018 Copyright 2018 Elsevier Elsevier Ltd. Ltd. All All rights rights reserved. reserved. Copyright © c 2018 Copyright Elsevierunder Ltd. All rights reserved. Selection and and peer-review Selection peer-review under responsibility responsibility of of the the scientific scientific committee committee of of the the 2017 2017 International International Conference Conference on on Identification, Identification, Selection andand peer-review under responsibility of the scientific committee of the 2017 International Conference on Identification, Information Information and Knowledge Knowledge in in the the Internet Internet of of Things Things (IIKI2017). (IIKI2017). Information and Knowledge in the Internet of Things (IIKI2017). Keywords: Keywords: Privacy preserving; mobile social network; location-based service; location privacy; anonymous region repartition. Privacy preserving; mobile social network; location-based service; location privacy; anonymous region repartition.
1. Introduction 1. Introduction Internet of Things (IoT), a trend of future networks, is immersing into many aspects of our personal and working of Thingsmore (IoT), a trend of future networks, is immersing into many aspects personal and working life,Internet it also provides comprehensive intelligent service. Social networks used widelyofinour mobile Internet catalyze life, it also provides more comprehensive intelligent service. Social networks used widely in mobile Internet catalyze mobile social networks (MSN), and users in MSN can not only acquire their own location information and sign in a mobile social networks (MSN), and users in MSN can not only acquire their own location information and sign in a location but also find nearby friends, access to location-based services (LBS) such as finding the nearest hotel, finding location but also find nearby friends, access to location-based services (LBS) such as finding the nearest hotel, finding directions, sharing action tracks, and so on [1, 2, 3, 4, 5, 6]. However, when we enjoy the convenience of LBS and directions, sharing action tracks, andconfront so on [1, 2, 3, 5, 6]. However, when wewhich enjoy isthe convenience LBS and MSN services, the mobile users also with the4,risk of location disclosure, a severe privacyofpreserving MSN services, the mobile users also confront with the risk of location disclosure, which is a severe privacy preserving concern [7, 8, 9, 10]. concern [7, 8,research 9, 10]. on the privacy protection technology based on location-based services (LBS) has attracted conRecently, Recently, research technology based onlocation location-based services (LBS) has attracted siderable interest. [11,on 12,the 13,privacy 14, 15].protection Besides the above described privacy preserving technologies, thereconare siderable interest. [11, 12, 13, 14, 15]. Besides the above described location privacy preserving technologies, there are ∗ ∗
Corresponding author. Tel.: +86-532-86057126 ; fax: +86-532-86057126. Corresponding Tel.: +86-532-86057126 ; fax: +86-532-86057126. E-mail address:author. [email protected], [email protected], [email protected], [email protected], [email protected] E-mail address: [email protected], [email protected], [email protected], [email protected], [email protected] c 2018 Elsevier Ltd. All rights reserved. 1877-0509 Copyright c 2018 1877-0509and Copyright Elsevier Ltd. Allof rights scientific reserved. committee of the 2017 International Conference on Identification, Information and Selection peer-review under responsibility 1877-0509 Copyright © 2018 Elsevier Ltd. Allthe rights reserved. Selection and peer-review responsibility of the scientific committee of the 2017 International Conference on Identification, Information and Knowledge in peer-review the Internet under of Things (IIKI2017). Selection and under responsibility of the scientific committee of the 2017 International Conference on Identification, Information Knowledge in theinInternet of Things (IIKI2017). and Knowledge the Internet of Things (IIKI2017). 10.1016/j.procs.2018.03.091
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Lina Ni et al. / Procedia Computer Science 129 (2018) 368–371 Lina Ni et al. / Procedia Computer Science 00 (2018) 000–000
369
a wealth of methods such as location data randomization [16, 17], fuzzification of space or time data [18], methods based on strategies and encryption [19], sensitive semantic based security anonymity mechanism [20, 21] In this paper, we focus on the location privacy preserving in MSN aiming at larger communication overhead, larger range and inaccuracy of query results for traditional anonymous schemes. 2. System Model As shown in Fig. 1, the principles of the model are as follows: 1. When the user requests the location query service, all the query contents, location information and parameters needed to be set are sent to the central anonymous servers. 2. After receiving the query information sent by users, according to certain rules, the central anonymous servers will generate an anonymous user set which meets the requirements, figure out the number of sub-anonymity regions and then partition them, a few scattered sub-anonymity regions are yielded. When the sub-anonymity regions meet the requirements, its central location is computed to replace corresponding sub-anonymity regions to send requests to the LBS server. 3. The LBS server handles the query information sent by the central anonymous servers and returns the query results. 4. After the refinement process, the central anonymous servers return the corresponding results to the users.
Fig. 1. Architecture of our system model.
3. Location Repartitioning Anonymous Region Scheme Combined with Fig. 2 (a) and (b), the basic idea of RPAR scheme is elaborated as follows: 1. The solid red dots represent the users initiating query who find k−1 users with whom the query users can form k-anonymity regions according to the nearest neighbor principle, and k users information set is recorded. It can be seen in Fig. 2 that k=14. 2. According to the parameter n, the number of sub-anonymity regions, the k users are divided into n sub-anonymity regions, so the number of users each sub-anonymity region contains is k =k/n=4. The mobile users (red dots) are as the center to search other nearest k −1 users and form the first sub-anonymity region. 3. A user from the rest of the users which is not in the first sub-anonymity region is randomly selected as the central point. According to the nearest neighbor principle, the sub-anonymity regions are formed with the user and other 3 users who is not the first sub-region form anonymous region, until the rest user number is 0 or below k . 4. The tail anonymity user set is repartitioned into other sub-anonymity regions according to the nearest neighbor principle, and the sub-anonymity regions are updated.
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Lina Ni et al. / Procedia Computer Science 129 (2018) 368–371 Lina Ni et al. / Procedia Computer Science 00 (2018) 000–000
(a) Before partition of k-anonymity region
3
(b) After partition of k-anonymity region
Fig. 2. Repartition of anonymous region.
5. The area of the sub-anonymity regions is calculated. If the total area of the sub-anonymity regions is greater than Amin , users in the maximum sub-anonymity region will be repartitioned until the total area of the sub-anonymity regions is not greater than Amin . 6. The central locations of all the sub-anonymity regions are computed which are used to replace their subanonymity regions to issue queries to the LBS servers. The pseudo-code is given in the Algorithm 1.
Algorithm 1 RPAR Input: uid, (x, y), k, l, n, Amin , M, other users’ location information in anonymous set stored in anonymous servers. Output: central locations (xi , yi ) and M. 1: k =k/n 2: form the first sub-anonymity region subAR1 , calculate its central location (x1 , y1 ) and area S 1 based on (x, y), k and other user location information. 3: for i=1 to n do 4: m=k−k ∗(i−1); 5: select user ui randomly from the rest of the tail anonymity user set um : ui =Random(um ); 6: form subARi ,calculate its central location (xi , yi ) and area S i according to the information of ui and parameter settings; 7: calculate total area of all the sub-anonymity regions: S A =S A +S i ; 8: end for 9: if k mod n== 0 then 10: if S A >Amin then 11: repartition the largest sub-anonymity region until S A
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371
4. Conclusions and Future Work Aiming at large communication overhead, large range and inaccuracy of query results for traditional anonymous schemes, this paper proposed an anonymous region repartition algorithm by studying the user’s location privacy preservation. The anonymous region is divided into several sub regions, the users’ real locations are replaced by the central location, and a repartition is carried out to solve the remaining users after the anonymous region segmentation. In the future, we will research on the location privacy preserving in the scenario of dense region. Acknowledgment This work is supported by NSF of China under Grant 61672321, 61771289 and 61373027, Training Program of the Major Research Plan of NSF of China under Grant 91746104, National Key R & D Programs Project of China under Grant 2017YFC0804406, Project of Shandong Province Higher Educational Science and Technology Program under Grant J13LN18, J15LN19, Open Project of Tongji University Embedded System and Service Computing of Ministry of Education of China under Grant ESSCKF 2015-02. References [1] T. Song, R. Li, B. Mei et al. (2017) “A privacy preserving communication protocol for IoT applications in smart homes.” IEEE Internet of Things Journal 4 (6): 1844–1852. [2] R. Li, T. Song, N. Capurso et al. (2017) “IoT applications on secure smart shopping system.” IEEE Internet of Things Journal 4 (6): 1945–1954. [3] T. Song, N. Capurso, X. Cheng et al. (2017) “Enhancing GPS with lane-level navigation to facilitate highway driving.” IEEE Transactions on Vehicular Technology 66 (6): 4579–4591. [4] X. Xiao, C. Chen, A. K. Sangaiah et al. (2017) “Cenlocshare: a centralized privacy-preserving location-sharing system for mobile online social networks,” Future Generation Computing Systems, to be published, doi:10.1016/j.future.2017.01.035. [5] H. Shen, G. Bai, M. Yang et al. (2017) “Protecting trajectory privacy: a user-centric analysis.” Journal of Network and Computer Applications 82: 128–139. [6] X. Zheng, G. Luo, and Z. Cai. (2018) “A fair mechanism for private data publication in online social networks.” IEEE Transactions on Network Science and Engineering, to be published, doi:10.1109/TNSE.2018.2679483. [7] Y. Liang, Z. Cai, Q. Han et al. (2017) “Location privacy leakage through sensory data.” Security and Communication Networks 2017 (11):1–12. [8] M. L. Damiani. (2012) “Privacy enhancing techniques for the protection of mobility patterns in LBS: Research issues and trends.” Communications in Computer and Information Science 244: 338–343. [9] C. Hu, W. Li, X. Cheng et al. (2017) “A secure and verifiable access control scheme for big data storage in clouds,” IEEE Transactions on Big Data, to be published, doi:10.1109/TBDATA.2016.2621106. [10] X. Zheng, Z. Cai, J. Yu et al. (2017) “Follow but no track: privacy preserved profile publishing in cyber-physical social systems.” IEEE Internet of Things Journal 4 (6): 1878–1878. [11] A. Sadilek, H. Kautz, and J. P. Bigham. (2012) “Finding your friends and following them to where you are”, on Web Search and Data Mining (WSDM) Proceeding of 5th ACM International Conference, Washington , ACM. [12] A. Ye, S. Lin, J. Ma et al. (2015) “An active diffusion location privacy protection method.” Acta Electronica Sinica 43 (7): 1362–1368. [13] K. Xing, C. Hu, J. Yu et al. (2017) “Mutual privacy preserving K-means clustering in social participatory sensing.” IEEE Transactions on Industrial Informatics 13 (4): 2066–2076. [14] Z. He, Z. Cai, J. Yu et al. (2017) “Cost-efficient strategies for restraining rumor spreading in mobile social networks.” IEEE Transactions on Vehicular Technology 66 (3): 2789–2800. [15] X. Liang, K. Zhang, X. Shen et al. (2014) “Security and privacy in mobile social networks: challenges and solutions.” IEEE Wireless Communications 21 (1): 33–41. [16] G. Sun, Y. Xie, D. Liao et al. (2017) “User-defined privacy location-sharing system in mobile online social networks.” Journal of Network and Computer Applications 86 (C): 34–45. [17] R. Kato, M. Iwata, T. Hara et al. (2012) “A dummy-based anonymization method based on user trajectory with pauses”, in Geographic Information Systems (AGIS) Proceeding of 20th ACM International Conference on Advances, Redondo Beach, ACM. [18] X. Pan, J. Xu, and X. Meng. (2012) “Protecting location privacy against location-dependent attacks in mobile services.” IEEE Transactions on Knowledge and Data Engineering 24 (8): 1506–1519. [19] I. T. Lien, H. s. Lin, Yu, J. R. Shieh et al. (2013) “A novel privacy preserving location-based service protocol with secret circular shift for K-NN search.” IEEE Transactions on Information Forensics and Security 8 (6): 863–873. [20] M. Li, Z. Qin, and C. Wang. (2014) “Sensitive semantics-aware personality cloaking on road-network environment.” International Journal of Security and Its Applications 8 (1): 133–146. [21] L. Zhang, Z. Cai, and X. Wang. (2016) “Fakemask: a novel privacy preserving approach for smartphones.” IEEE Transactions on Network and Service Management 13 (2): 335–348.