LI Yongkai, LIU Shubo, LI Dan, et al., “Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 1104-1110, 2018, doi: 10.1049/cje.2017.08.008
Citation: LI Yongkai, LIU Shubo, LI Dan, et al., “Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 1104-1110, 2018, 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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