Privacy Leakage Via De-Anonymization And Aggregation In Heterogeneous Social Networks

Abstract

Although the approach represents a personal commitment, guidance, and recommendation, user profile accumulation of several social networks would result in significant privacy breaches. In this proposal, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS). The NHDS began using a graphical design of the network to dramatically reduce the size of the set of candidates and then used the information to identify the profile that the user mapping and the degree of belief were high. Evaluation of performance data in real social networks shows that NHDS significantly exceeds the original schedule. Finally, we conduct a scientific study of the loss of integrity of results raised several networks based on four sets of social network data. Our findings show that 39.9% of the information is captured in anonymity and the anonymization ratio is 84%. The second policy leaks the population and the interests of users also under consideration, indicating the potential for leaks is recognized privately..