个性化时空数据隐私保护

智能移动终端的普及导致收集的时空数据中个人位置隐私、签到数据隐私、轨迹隐私等敏感信息容易泄露，且当前研究分别针对上述隐私泄露单独提出保护技术，而没有面向用户给出防止上述隐私泄露的个性化时空数据隐私保护方法。针对这个问题，提出一种面向时空数据的个性化隐私保护模型（p，q，ε）-匿名和基于该模型的个性化时空数据隐私保护（PPP_ST）算法，从而对用户个性化设置的隐私数据（位置隐私、签到数据隐私和轨迹隐私）加以保护。设计了启发式规则对时空数据进行泛化处理，保证了发布数据的可用性并实现了时空数据的高可用性。对比实验中PPP_ST算法的数据可用率比个性化信息数据K-匿名（IDU-K）和个性化Clique Cloak（PCC）算法分别平均高约4.66%和15.45%。同时，设计了泛化位置搜索技术来提高算法的执行效率。基于真实时空数据进行实验测试和分析，实验结果表明PPP_ST算法能有效地保护个性化时空数据隐私。

Personalized privacy protection for spatio-temporal data

Due to the popularity of smart mobile terminals, sensitive information such as personal location privacy, check-in data privacy and trajectory privacy in the collected spatio-temporal data are easy to be leaked. In the current researches, protection technologies are proposed for the above privacy leakages respectively, and there is not a personalized spatio-temporal data privacy protection method to prevent the above privacy leakages for users. Therefore, a personalized privacy protection model for spatio-temporal data named (p,q,ε)-anonymity and a Personalized Privacy Protection for Spatio-Temporal Data (PPP_ST) algorithm based on this model were proposed to protect the users' privacy data with personalized settings (location privacy, check-in data privacy and trajectory privacy). The heuristic rules were designed to generalize the spatio-temporal data to ensure the availability of the published data and realize the high availability of spatio-temporal data. In the comparison experiments, the data availability rate of PPP_ST algorithm is about 4.66% and 15.45% higher than those of Information Data Used through K-anonymity (IDU-K) and Personalized Clique Cloak (PCC) algorithms on average respectively. At the same time, the generalized location search technology was designed to improve the execution efficiency of the algorithm. Experiments and analysis were conducted based on real spatio-temporal data. Experimental results show that PPP_ST algorithm can effectively protect the privacy of personalized spatio-temporal data.