Release Connection Fingerprints in Social Networks Using Personalized Differential Privacy

There are many benefits of publication social networks statistics for societal or commercial purposes, such as political advocacy and product recommendation. It is very challenging to protect the privacy of individuals in social networks while ensuring a high accuracy of the statistics. Moreover, most of the existing work on differentially private social network publication ignores the facts that different users may have different privacy preferences and there also exists a considerable amount of users whose identities are public. In this paper, we aim to release the number of public users that a private user connects to within n hops (denoted as n-range Connection Fingerprints,or nrange CFPs for short) regarding user-level personalized privacy preferences. To this end, we proposed two schemes, DEBA and DUBA-LF, for privacy-preserving publication of the CFPs on the base of personalized differential privacy(PDP), and conduct 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.