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A stochastic location privacy protection scheme for edge computing

1 School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China
2 School of Computer Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
3 Department of Computer Science, King Saud University, 11362, Saudi Arabia

Special Issues: Security and Privacy Protection for Multimedia Information Processing and communication

Location-based Service has become the fastest growing activity related service that people use in their daily life due to the boom of location-aware mobile devices. In edge computing along with the benefits brought by LBS, privacy preservation becomes a more challenging issue because of the nature of the paradigm, in which peers may cooperate with each other to collect and analyze user’s location data. To avoid potential information leakage and usage, user’s exact location should not be exposed to the edge node. In this paper, we propose a stochastic location privacy protection scheme for edge computing, in which the geographical distribution of surrounding users is obtained by analyzing proposed long-term density map and short-term density map. The cloaking scheme transfers user’s exact location to a cloaked location to satisfy predefined probability of having k-users in that area. Our scheme does not reveal any exact location information, thus it is practicable for the real scenario when edge computing is honest but curious. Extensive experimental results are conducted to verify the efficiency and effectiveness of our method. By varying the privacy protection requirements, the corresponding performance have been examined and discussed.
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Keywords location-based service; edge computing; location privacy

Citation: Yuan Tian, Biao Song, Mznah Al Rodhaan, Chen Rong Huang, Mohammed A. Al-Dhelaan, Abdullah Al-Dhelaan, Najla Al-Nabhan. A stochastic location privacy protection scheme for edge computing. Mathematical Biosciences and Engineering, 2020, 17(3): 2636-2649. doi: 10.3934/mbe.2020144

References

  • 1. M. Satyanarayanan, The emergence of edge computing, Computer, 50 (2017), 30-39.
  • 2 L. Xiong, Y. Shi, On the Privacy-Preserving Outsourcing Scheme of Reversible Data Hiding over Encrypted Image Data in Cloud Computing, CMC Comput. Mater. Con., 55 (2018), 523-539.
  • 3. A. Alabdulkarim, M. Al-Rodhaan, Y. Tian A. Al-Dhelaan, A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest, CMC Comput. Mater. Con., 58 (2019), 585-601.
  • 4. X. Du, H. H. Chen, Security in Wireless Sensor Networks, IEEE Wireless Commun., 15 (2008), 60-66.
  • 5. A. J. Paverd, A. Martin, I. Brown, Modelling and automatically analysing privacy properties for honest-but-curious adversaries, Univ. Oxford Tech. Rep., 2014 (2014).
  • 6. H. Kido, Y. Yanagisawa, T. Satoh, An anonymous communication technique using dummies for location-based services, ICPS '05, International Conference on Pervasive Services, 2005, 88-97. Available from: https://ieeexplore_ieee.xilesou.top/abstract/document/1506394.
  • 7. T. Ma, Y. Zhang, J. Cao, J. Shen, M. Tang, Y. Tian, KDVEM: A k-degree anonymity with Vertex and Edge Modification algorithm, Computing, 97 (2015): 1165-1184.
  • 8. M. Gruteser, D. Grunwald, Anonymous usage of location-based services through spatial and temporal cloaking, Proceedings of the 1st international conference on Mobile systems, applications and services, ACM, 2003, 31-42.
  • 9. R. Shokri, C. Troncoso, C. Diaz, J. Freudiger, J. P. Hubaux, Unraveling an Old Cloak: K-anonymity for Location Privacy, Proceeding WPES'10 Proceedings of the 9th annual ACM workshop on Privacy in the electronic society, 2010, 115-118. Available from: https://dl_acm.xilesou.top/citation.cfm?id=1866936.
  • 10. B. Gedik, L. Liu, Protecting location privacy with personalized k-anonymity: Architecture and algorithms, IEEE Trans. Mobile Comput., 7 (2008), 1-18.
  • 11. M. F. Mokbel, C. Y. Chow, W. G. Aref, The new casper: Query processing for location services without compromising privacy, VLDB '06 Proceedings of the 32nd international conference on Very large data bases, 763-774. Available from: https://dl_acm.xilesou.top/citation.cfm?id=1164193.
  • 12. K. W. Tan, Y. Lin, K. Mouratidis, Spatial cloaking revisited: Distinguishing information leakage from anonymity, International Symposium on Spatial and Temporal Databases. Springer, Berlin, Heidelberg, 2009, 117-134. Available from: https://link_springer.xilesou.top/chapter/10.1007/978-3-642-02982-0_10.
  • 13. T. Xu, Y. Cai, Feeling-based location privacy protection for location-based services, CCS '09 Proceedings of the 16th ACM conference on Computer and communications security, 2009. 348-357. Available from: https://dl_acm.xilesou.top/citation.cfm?id=1653704.
  • 14. C. Bettini, X. S. Wang, S. Jajodia, Protecting privacy against location-based personal identification, Workshop on Secure Data Management, Springer, Berlin, Heidelberg, 2005, 185-199. Available from: https://link_springer.xilesou.top/chapter/10.1007/11552338_13.
  • 15. K. Sampigethaya, L. Huang, M. Li, R. Poovendran, K. Matsuura, K. Sezaki, Caravan: Providing location privacy for VANET, Proceeding Embedded Security in Cars (ESCAR) Workshop, 2005, 13-15. Available from: https://labs.ece.uw.edu/nsl/papers/ESCAR-05.pdf.
  • 16. G. Ghinita, P. Kalnis, S. Skiadopoulos, Prive: Anonymous location-based queries in distributed mobile systems, WWW '07 Proceedings of the 16th international conference on World Wide Web, 371-380, 2007. Available from: https://dl_acm.xilesou.top/citation.cfm?id=1242572.1242623.
  • 17. J. Xu, X. Tang, H. Hu, J. Du, Privacy-consious location-based queries in mobile environments, IEEE Trans. Parallel Distrib. Syst., 21 (2010), 313-326.
  • 18. T. Ma, J. Jia, Y. Xue, Y. Tian, A. Al-Dhelaan, M. Al-Rodhaan, Protection of location privacy for moving kNN queries in social networks, Appl. Soft Comput., 66 (2018), 525-532.
  • 19. Y. Tian, M. M. Kaleemullah, M. A. Rodhaan, B. Song, A. Al-Dhelaan, T. Ma, A privacy preserving location service for cloud-of-things system, J. Parallel Distrib. Comput., 123 (2019), 215-222.
  • 20. T. Wang, J. Zeng, Z. A. Bhuiyan, H. Tian, Y. Cai; Y. Chen, et al., Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services, IEEE Access, 5 (2017), 7692-7701.
  • 21. C. Dwork, M. Naor, T. Pitassi, G. N. Rothblum, Differential privacy under continual observation, STOC '10 Proceedings of the forty-second ACM symposium on Theory of computing, 715-724, 2010. Available from: https://dl_acm.xilesou.top/citation.cfm?id=1806787.
  • 22. C. Dwork, The promise of different privacy: A tutorial on algorithmic techniques, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science, 2011. Available from: https://www.microsoft.com/en-us/research/wp-content/uploads/2011/10/PID2016981.pdf.
  • 23. N. Li, T. Li, S. Venkatasubramanian, t-closeness: Pricavy beyond k-anonymity and l-diversity, 2007 IEEE 23rd International Conference on Data Engineering, 2007, 106-115. Available from: https://ieeexplore_ieee.xilesou.top/abstract/document/4221659.
  • 24. L. Zheng, H. Yue, Z. Li, X. Pan, M. Wu, F. Yang, k-Anonymity Location Privacy Algorithm Based on Clustering, IEEE Access, 6 (2017), 28328-28338.

 

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