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A novel ε-sensitive correlation indistinguishable scheme for publishing location data


Autoři: Wang Bin aff001;  Zhang Lei aff001;  Zhang Guoyin aff001
Působiště autorů: College of Computer Science and Technology, Harbin Engineering University, Harbin, PR China aff001;  College of Information Science and Electronic Technology, Jiamusi University, Jiamusi, PR China aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226796

Souhrn

Nowadays, location based service (LBS) is one of the most popular mobile apps and following with humongous of location data been produced. The publishing of location data can provide benefit for promoting the quality of service, optimizing the commercial environment as well as harmonizing the infrastructure construction. However, as location data may contain some sensitive or confidential information, the publishing may reveal privacy and bring hazards. So the published data had to be disposed to protect the privacy. In order to cope with this problem, a number of algorithms based on the strategy of k-anonymity were proposed, but this is not enough for the privacy protection, as the correlation between the sensitive region and the background knowledge can be used to infer the real location. Thus, consider about this condition, in this paper a ε-sensitive correlation privacy protection scheme is proposed, and provides correlation indistinguishable to the location data. In this scheme, entropy is first used to determine the location centroid of each cell to build up the voronoi diagram. Then the coordinate of the untreated location data that is located in the cell is transferred into the centroid vicinity. Accordingly, the sensitive correlation is destroyed by the coordinate of each published data. The process of transferring the location data is determined by metrics of ε-sensitive correlation privacy, and is rigorous in mathematical justification. At last, security analysis is proposed in this paper to verify the privacy ability of our proposed algorithm based on voronoi diagram and entropy, and then we utilize the comparative experiment to further affirm the advantage of this algorithm in the location data privacy protection as well as the availability of published data.

Klíčová slova:

Algorithms – Cell cycle and cell division – Data mining – Data processing – Data reduction – Entropy – Social communication – Telecommunications


Zdroje

1. Wang H, Zhang Z, Taleb T. Editorial: Special Issue on Security and Privacy of IoT. World Wide Web. 2018;21(1):1–6. doi: 10.1007/s11280-017-0490-9

2. Shu J, Jia X, Kan Y, Hua W. Privacy-Preserving Task Recommendation Services for Crowdsourcing. IEEE Transactions on Services Computing. 2018;PP(99):1–.

3. Sun X, Wang H, Li J, Pei J. Publishing anonymous survey rating data. Data Mining and Knowledge Discovery 2011;3(23):379–406.

4. Fung BCM, Wang K, Chen R, Yu PS. Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys. 2010;42(4):14. doi: 10.1145/1749603.1749605 WOS:000279357800002.

5. Yabo X, Fung BCM, Ke W, Fu AWC, Jian P, editors. Publishing sensitive transactions for itemset utility. Data Mining, 2008 ICDM '08 Eighth IEEE International Conference on; 2008 15–19 Dec. 2008.

6. Bonchi F, Lakshmanan LVS, Wang H. Trajectory anonymity in publishing personal mobility data. SIGKDD Explor Newsl. 2011;13(1):30–42. doi: 10.1145/2031331.2031336

7. Sui P, Wo T, Wen Z, Li X, Ieee. Privacy-Preserving trajectory publication against parking point attacks. 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (Uic/Atc) Ubiquitous Intelligence and Computing. 2013:569–74. doi: 10.1109/uic-atc.2013.75 WOS:000346129800078.

8. Zhao J, Zhang Y, Li XH, Ma JF. A trajectory privacy protection approach via trajectory frequency suppression. Chinese Journal of Computers. 2014;37(10):2096–106.

9. Wang C, Liu H, Wright K-L, Krishnamachari B, Annavaram M. A privacy mechanism for mobile-based urban traffic monitoring. Pervasive and Mobile Computing. 2015;20(2015):1–12.

10. Gruteser M, Grunwald D, editors. Anonymous usage of location-based services through spatial and temporal cloaking. Proceedings of the 1st international conference on Mobile systems, applications and services; 2003; San Francisco, California. 1189037: ACM.

11. Xiao Z, Xu J, Meng X, editors. p-Sensitivity: A semantic privacy-protection model for location-based services. International Conference on Mobile Data Management Workshops; 2008.

12. Fuyu L, Hua KA, Ying C, editors. Query l-diversity in location-based services. Mobile Data Management: Systems, Services and Middleware, 2009 MDM '09 Tenth International Conference on; 2009 18–20 May 2009.

13. Wang Y, Xia Y, Hou J, Gao SM, Nie X, Wang Q. A fast privacy-preserving framework for continuous location-based queries in road networks. Journal of Network and Computer Applications. 2015;53(2015):57–73. doi: 10.1016/j.jnca.2015.01.004 WOS:000356184900005.

14. Lei Z, Chun-guang M, Song-tao Y, Xiao-dong Z. A real-time similar trajectories generation algorithm for trajectories differences identification resistance. Journal of Harbin Engineering University. 2017;2017(07):1173–8.

15. Ma C, Zhang L, Yang S, Zheng X, Ke P. Achieve personalized anonymity through query blocks exchanging. China Communications. 2016;13(11):106–18.

16. Chunguang M, Lei Z, Songtao Y, Xiaodong Z. Hiding Yourself Behind Collaborative Users When Using Continuous Location-Based Services. Journal of Circuits, Systems and Computers. 2017;26(07):1750119:1-:25.

17. Niu B, Zhu XY, Li QH, Chen J, Li H. A novel attack to spatial cloaking schemes in location-based services. Future Generation Computer Systems-the International Journal of Grid Computing and Escience. 2015;49(2015):125–32. doi: 10.1016/j.future.2014.10.026 WOS:000355062700012.

18. Dargahi T, Ambrosin M, Conti M, Asokan N. ABAKA: A novel attribute-based k-anonymous collaborative solution for LBSs. Computer Communications. 2016;85(2016):1–13. doi: 10.1016/j.comcom.2016.03.002 WOS:000376830900001.

19. Zhang L, Ma C, Yang S, Zheng X. Probability Indistinguishable: A Query and Location Correlation Attack Resistance Scheme. Wireless Personal Communications. 2017;97(4):6167–87.

20. Lei Z, Chun-guang M, Song-tao Y, Zeng-peng L. CP-ABE based users collaborative privacy protection scheme for continuous query. Journal on Communications. 2017;38(09):76–85.

21. Zhang L, Li J, Yang S, Wang B. Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query. Wireless Personal Communications. 2017;96(2):2305–22.

22. Li Z, Xiang C, Wang C. Oblivious Transfer via Lossy Encryption from Lattice-Based Cryptography. Wireless Communications and Mobile Computing.

23. Li Z, Ma C, Ding W. Achieving Multi-Hop PRE via Branching Program. IEEE Transactions on Cloud Computing. 2017;PP(99):1-.

24. Li Z, Ma C, Ding W. Leakage Resilient Leveled FHE on Multiple Bit Message. IEEE Transactions on Big Data. 2017;PP(99):1–.

25. Chen R, Fung BCM, Mohammed N, Desai BC, Wang K. Privacy-preserving trajectory data publishing by local suppression. Information Sciences. 2013;231(2013):83–97. doi: 10.1016/j.ins.2011.07.035 WOS:000316836600007.

26. Terrovitis M, Poulis G, Mamoulis N, Skiadopoulos S. Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories. IEEE Transactions on Knowledge & Data Engineering. 2017;29(7):1466–79.

27. Chow C-Y, Mokbel MF. Trajectory privacy in location-based services and data publication. SIGKDD Explor Newsl. 2011;13(1):19–29. doi: 10.1145/2031331.2031335

28. Lu Q, Wang C, Xiong Y, Xia H, Huang W, Gong X. Personalized Privacy-Preserving Trajectory Data Publishing. Chinese Journal of Electronics. 2017;26(2):285–91.

29. Zheng X, Cai Z, Yu J, Wang C, Li Y. Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems. IEEE Internet of Things Journal. 2017;PP(99):1–.

30. Lei Z, Lili H, Desheng L, Jing L, Qingfeng J, Qi Y. An Attribute Generalization Mix-Zone Without Privacy Leakage. IEEE Access. 2019;7(1):57088–99. doi: 10.1109/ACCESS.2019.2898996

31. Li M, Zhu L, Zhang Z, Xu R. Achieving Differential Privacy of Trajectory Data Publishing in Participatory Sensing. Information Sciences. 2017;400–401:1–13.

32. Cicek AE, Nergiz ME, Saygin Y. Ensuring location diversity in privacy-preserving spatio-temporal data publishing. Vldb Journal. 2014;23(4):609–25. doi: 10.1007/s00778-013-0342-x WOS:000339904500005.

33. Lin C, Wu GW, Yu CW. Protecting location privacy and query privacy: a combined clustering approach. Concurrency and Computation-Practice & Experience. 2015;27(12):3021–43. doi: 10.1002/cpe.3244 WOS:000358507500009.

34. Verma P, Boghey R, Rai S, Verma P, Boghey R, Rai S. Classifying Student’s Learning Experience using Improved Apriori and CART. International Journal of Computer Applications. 2017;174(1):34–40.


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