A trust-grained personalized privacy-preserving scheme for big social data

In the age of big data, the rapid development of social networking applications has become an improtant data source, while the massive collection of personal data leads to significant privacy concerns. Differential privacy emerged as an effective tool to get access to useful information while provide strong privacy guarantees. However, most the current proposed solutions suppose that all individuals across the network require a uniform level of privacy protection, which rules out of individuals’ personalized requirements. Aiming at solving this problem, in this paper, we propose a trust-grained personalized differential privacy mechanism, called TGDP, by combining the notion of trust. Specifically, whenever a user wants to get another user’s personal information, the proposed mechanism returns a corresponding private response in which the privacy level selected for each individual depend on the trust value between them in the network. Compared with traditional methods, the scheme can provide a fine-grained differential privacy protection method, while guarantee the utility of social networks. Finally, the scheme is evaluated analytically, and demonstrated experimentally on the real-world data, which reflects its effectiveness and utility