Abstract:

Due to randomness of noise and complexity of weighted social networks,traditional privacy protection methods cannot balance both data privacy and utility issues in social networks.This paper addresses these problems,combines histogram statistics and non-interactive differential privacy query model,and proposes a statistical releasing method for the histogram of weighted-edges in social networks.This method regards the statistical histogram of weighted-edges as the query result and designs the low-sensitivity Laplace noise random perturbation algorithm,which realizes the differential privacy protection of social relations.In order to reduce errors,the community structure entropy is introduced to divide the user nodes of the social network into several sub-communities,with the improved stochastic perturbation algorithm proposed.The social relationship is divided by community as a unit and Laplace noise is injected,so that each sequence of community satisfies the differential privacy with the social relationship protected from the community level.In addition,the characteristics of one-dimensional structural entropy are used to measure the overall privacy protection degree of the algorithm with respect to the weighted social network.By theoretical analysis and experimental results,the privacy protection algorithm proposed in this paper has a higher protection degree than the comparison algorithm for node degree identification,which achieves a better privacy protection effect.Meanwhile,it can meet the requirements of differential privacy in large social networks and maintain a high data utility of the weighted social network.

Key words: weighted social network, differential privacy, histogram release, community structure entropy, edge relation grouping