基于假位置选择的位置隐私脱敏算法

传统位置隐私脱敏技术容易遭受背景知识攻击和单点攻击，造成用户位置隐私泄露，危及其生命财产安全。针对此问题，提出了一种基于假位置选择的位置隐私脱敏算法DLPD（dummy location privacy desensitization）。首先，通过衡量位置敏感程度、实行偏移优化、过滤位置查询概率来构造假位置候选集；其次，改善位置分布筛选假位置；最终，构造安全匿名集实现位置隐私脱敏。实验证明，该算法增强了隐私保护强度，提高了隐私保护的有效性，同时，降低了开销，具有实用性。

Location privacy desensitization algorithm based on dummy location selection

Traditional location privacy desensitization algorithms are susceptible to background knowledge attack and single point attack, which causes users'' location privacy to be leaked and endangers their life and property safety. In order to solve the problem, this paper proposed a location privacy desensitization algorithm based on dummy location selection, called DLPD. Initially, it constructed a dummy location candidate set by measuring location sensitivity, implementing offset optimization and filtering location query probability. Furthermore, it improved the location distribution to filter dummy locations. Eventually, it constructed a secure anonymous set to achieve location privacy desensitization. Experiments demonstrate that the algorithm enhances the privacy protection strength and improves the effectiveness of privacy protection. Meanwhile, it reduces the overhead and has practicality.