Privacy preserving based on differential privacy for weighted social networks - Li-hui LAN; Shi-guang JU (Tongxin xuebao)
Focusing on the weak protection problems in privacy preservation of weighted social networks publication,a privacy preserving method based on differential privacy was put forward for strong protection of edges and edge weights.The WSQuery query model was proposed meeting with differential privacy on weighted social networks,could capture the structure of weighted social networks and returned the triple sequences as the query result set.The WSPA algorithm was designed according to the WSQuery model,could map the query result set into a real number vector and injected Laplace noise into the vector to realize privacy protection.The LWSPA algorithm was put forward because of the high error of the WSPA algorithm,partitioned the triples sequence of the query results into multiple subsequences,constructed the algorithms for each subsequence according with differential privacy and reduced the error and improved the data util-ity.The experimental results demonstrate that the proposed method can provide strong protection for privacy information,simultaneously the utility of the released weighted social networks is still acceptable.