基于地理社交网络的频繁位置隐私保护算法

针对地理社交网络中以频繁位置为背景知识的攻击导致用户身份泄露的问题，提出一种基于地理社交网络的频繁位置隐私保护算法。首先，根据用户对位置访问的频次设置频繁位置并为每个用户建立频繁位置集合；然后按照背景知识的不同，将频繁位置的子集组成超边，把不满足匿名参数k的超边以用户偏离和位置偏离最小值为优化目标进行超边重组；最后，通过仿真实验表明，与（k，m）-anonymity算法相比，在频繁位置为3的情况下，该算法在Gowalla数据集上用户偏离度以及位置偏离度分别平均降低了约19.1%和8.3%，在Brightkite数据集上分别平均降低了约22.2%和10.7%，因此所提算法能够有效保护频繁位置的同时降低用户和位置偏离度。

Frequent location privacy-preserving algorithm based on geosocial network

Focusing on the attack of frequent location as background knowledge causing user identity disclosure in geosocial network, a privacy-preserving algorithm based on frequent location was proposed. Firstly, The frequent location set was generated by the frequency of user check-in which was allocated for every user. Secondly,according to the background knowledge, hyperedges were composed by frequent location subset. Some hyperedges were remerged which did not meet anonymity parameter k, meanwhile the minimum bias of user and bias of location were chosen as hyperedges remerging metrics. Finally, in the comparison experiments with (k,m)-anonymity algorithm, when the background knowledge was 3, the average bias of user and bias of location were decreased by about 19.1% and 8.3% on dataset Gowalla respectively, and about 22.2% and 10.7% on dataset Brightkite respectively. Therefore, the proposed algorithm can effectively preserve frequent location privacy, and reduces bias of user and location.