基于差分隐私的权重社会网络隐私保护

针对权重社会网络发布隐私保护中的弱保护问题，提出一种基于差分隐私模型的随机扰动方法可实现边及边权重的强保护。设计了满足差分隐私的查询模型-WSQuery，WSQuery模型可捕获权重社会网络的结构，以有序三元组序列作为查询结果集；依据WSQuery模型设计了满足差分隐私的算法-WSPA，WSPA算法将查询结果集映射为一个实数向量，通过在向量中注入Laplace噪音实现隐私保护；针对WSPA算法误差较高的问题提出了改进算法-LWSPA，LWSPA算法对查询结果集中的三元组序列进行分割，对每个子序列构建满足差分隐私的算法，降低了误差，提高了数据效用。实验结果表明，提出的隐私保护方法在实现隐私信息的强保护同时使发布的权重社会网络仍具有可接受的数据效用。

Privacy preserving based on differential privacy for weighted social networks

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 utility. 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.