Personalized privacy protection in social networks

Abstract

Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user's personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker's background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users' protection requirements. We verify the effectiveness of the framework through extensive experiments.

Conservative or liberal? Personalized differential privacy

“…Subsequently a slew of related approaches (e.g., [31], [28], [22], [29]) extended this to other methods for achieving k-anonymity and related definitions. However, these definitions have been criticized due to the feasibility of attacks that can lead to disclosure of sensitive attributes [16], [13], [32], and more robust notions, led by differential privacy, are now preferred.…”

Privacy Protection in Mobile Social Network in the Context of Big Data

“…The main idea of the node K-anonymity [2][3][4][5][6] and subgraph K-anonymity [7][8][9][10][11][12][13]: there are at least k candidates in the anonymized social network while attackers identify a special object based on background knowledge, that is to say, the probability of privacy disclosure is less than 1/K.…”

A Personalized Privacy Preserving Method for Publishing Social Network Data

“…Input: the original graph G, the degree sequence P of G, the PKDLD sequence P for all (node u in V sdif f ) do 4. if (in V sdif f ,there is a node v, d(u, v) end if 7.…”