LI Yongkai, LIU Shubo, LI Dan, WANG Jun. Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy[J]. Chinese Journal of Electronics, 2018, 27(5): 1104-1110. doi: 10.1049/cje.2017.08.008
Citation: LI Yongkai, LIU Shubo, LI Dan, WANG Jun. Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy[J]. Chinese Journal of Electronics, 2018, 27(5): 1104-1110. doi: 10.1049/cje.2017.08.008

Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy

doi: 10.1049/cje.2017.08.008
Funds:  This work is supported by the Special S&T Project on Treatment and Control of Water Pollution (No.2017ZX07108-001-003), the National Natural Science Foundation of China (No.41671443), and the Fundamental Research Funds for the Central Universities (No.2042017gf0038, No.2042017kf0044).
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  • Corresponding author: LIU Shubo (corresponding author) was born in 1970. He received the Ph.D. degree in communication and information system from Wuhan University. He is a professor of School of Computer, Wuhan University. His research interests include security and privacy issues in wireless networks and social networks. (Email:liu.shubo@whu.edu.cn)
  • Received Date: 2016-11-01
  • Rev Recd Date: 2017-03-23
  • Publish Date: 2018-09-10
  • In social networks, different users may have different privacy preferences and there are many users with public identities. Most work on differentially private social network data publication neglects this fact. We aim to release the number of public users that a private user connects to within n hops, called n-range Connection fingerprints (CFPs), under user-level personalized privacy preferences. We proposed two schemes, Distance-based exponential budget absorption (DEBA) and Distancebased uniformly budget absorption using Ladder function (DUBA-LF), for privacy-preserving publication of the CFPs based on Personalized differential privacy (PDP), and we conducted a theoretical analysis of the privacy guarantees provided within the proposed schemes. The implementation showed that the proposed schemes are superior in publication errors on real datasets.
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  • G. Cormode, D. Srivastava, S. Bhagat, et al., “Class-based graph anonymization for social network data”, Proceedings of the VLDB Endowment, Vol.2, No.1, pp.766-777, 2009.
    M. Hay, G. Miklau, D. Jensen, et al., “Resisting structural reidentification in anonymized social networks”,The VLDB Journal, Vol.19, No.6, pp.797-823, 2010.
    Y. Wang and B. Zheng, “Preserving privacy in social networks against connection fingerprint attacks”, IEEE International Conference on Data Engineering, Seoul, Korea, pp.54-65, 2015.
    C. Dwork, “Differential privacy”, International Colloquium on Automata, Languages and Programming, Venice, Italy, pp.1-12, 2006.
    M. Yuan, L. Chen and P.S. Yu, “Personalized privacy protection in social networks”, Proceedings of the VLDB Endowment, Vol.4, No.2, pp.141-150. 2010.
    Z. Jorgensen, T. Yu and G. Cormode, “Conservative or liberal? Personalized differential privacy”, IEEE International Conference on Data Engineering, Seoul, Korea, pp.1023-1034, 2015.
    G. Times, “Media, govt, organizations get hooked on weibo: Report”, available at http://www.globaltimes.cn/content/757560.shtml, 2013-1-23.
    F.D. McSherry, “Privacy integrated queries: An extensible platform for privacy-preserving data analysis”, Proceedings of the ACM SIGMOD International Conference on Management of data, Providence, Rhode Island, USA, pp.19-27, 2010.
    C. Dwork, F. Mcsherry, K. Nissim, et al., “Calibrating noise to sensitivity in private data analysis”, Proceedings of the Third Conference on Theory of Cryptography, New York, USA, pp.265-284, 2006.
    F. McSherry and K. Talwar, “Mechanism design via differential privacy”, Foundations of Computer Science Annual Symposium on, Providence, Rhode Island, pp.94-103, 2007.
    K. Nissim, S. Raskhodnikova and A. Smith, “Smooth sensitivity and sampling in private data analysis”, Proceedings of the Thirty-ninth Annual ACM Symposium on Theory of Computing, San Diego, California, USA, pp.75-84, 2007.
    P. Mittal, C. Papamanthou and D. Song, “Preserving link privacy in social network based systems”, The Network and Distributed System Security Symposium, San Diego, California, USA, 2013.
    C. Liu and P. Mittal, “LinkMirage: Enabling privacy-preserving analytics on social relationships”, The Network and Distributed System Security Symposium, San Diego, California, USA, 2016.
    M. Hay, G. Miklau, D. Jensen, et al., “Anonymizing social networks”, University of Massachusetts Technical Report, No.07-19, 2007.
    L. Sweeney, “K-anonymity: A model for protecting privacy”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.10, No.5, pp.557-570, 2002.
    M. Hay, C. Li, G. Miklau, et al., “Accurate estimation of the degree distribution of private networks”, IEEE International Conference on Data Mining, Miami, FL, USA, pp.169-178, 2009.
    E. Shen and T. Yu, “Mining frequent graph patterns with differential privacy”, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, pp.545-553, 2013.
    J. Zhang, G. Cormode, C. Procopiuc, et al., “Private release of graph statistics using ladder functions”, ACM International Conference on Management of Data, Melbourne, VIC, Australia, pp.731-745, 2015.
    G. Kellaris, S. Papadopoulos, X. Xiao, et al., “Differentially private event sequences over infinite streams”, Proceedings of the VLDB Endowment, Vol.7, No.12, pp.1155-1166, 2014.
    L.A. Adamic and N. Glance, “The political blogosphere and the 2004 U.S. election: Divided they blog”, International Workshop on Link Discovery, Chicago, IL, USA, pp.36-43, 2005.
    J. Leskovec, J. Kleinberg and C. Faloutsos, “Graph evolution: Densification and shrinking diameters”, ACM Transactions on Knowledge Discovery from Data (TKDD), Article 2, 41 pages, doi=10.1145/1217299.1217301, 2007.
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