关键词: 社交网络, 隐私保护, 邻近图, 隐私挖掘, 维纳滤波

Abstract: With the widespread popularity of social network platforms, privacy disclosure has become a focus when users share personal information. There is a lack of unified evaluation on the privacy preserving methods for noisy social network. From the perspective of privacy mining, we choose the wiener filtering of signal processing which can automatically suppress and eliminate noise on the neighbor graph, and without considering background knowledge. Then we propose a social network privacy preserving intensity evaluation model, and design a privacy protection intensity evaluation algorithm. To verify the feasibility of the algorithm, this paper uses statistical properties of the undirected graph to measure privacy preserving intensity. The experiments conducted also show that the evaluation method can filter out some noise in the neighbor graph, achieve the purpose of the privacy preserving intensity evaluation, and provide guidance for theoretical research on privacy preserving of social network.

Key words: social network, privacy preserving, neighbor graph, privacy mining, wiener filtering