Differential Privacy Algorithm for Privacy Protection in Weighted Social Network
摘要
In order to solve the edge weight privacy leakage problem of social network,a privacy protection algorithm for weighted social network is proposed.The undirected weighted graph is used to represent the social network,the edge weight sequence is treated as an unassigned histogram,and the weight containing sensitive information is added to the Laplace noise to satisfy the differential privacy protection requirement.In order to reduce noise,the buckets with the same count in histogram are merged into groups,and the differential privacy protection requirements are guaranteed according to the k-indiscernibility between groups,and the shortest path of the network is kept unchanged by consistent reasoning on the original weight sequence.Theoretical analysis and experimental results show that the proposed algorithm can meet the requirements of differential privacy protection and improve the accuracy and practicability of information release.