基于位置服务中位置及轨迹隐私保护理论与方法研究

The location and trajectory privacy protection in location based service (LBS) is the topic of our proposal. For the location privacy protection, the game theory will be employed to make a tradeoff between the location privacy and the quality of service provided, under the guide of which location privacy protection schemes will be devised. Then a specific encryption algorithm will be devised, with which secue and efficient range queries and k nearest neighbors (kNN) queries on the encrypted database will be realized, to support the LBS location privacy protection in the cloud computing. Meanwhile, we will focus on the location privacy protection for the group users in the nearest neighbor query. The users in the group will derive a point that is located nearest away from the group users, while the group users do not need to reveal their respective locations. Through which, the group will get an accurate location service. For the trajectory privacy protection, an adaptive trajectory k-anonymity method with the location-awareness will be proposed, and the user adaptively select a anonymity set based on the deffereiated requests for the privacy of the different points in the trajectory. Based on which, the k trajectories that can provide the maxium privacy protection will be constructed between the contiguous sample points. Last, considering that different users have different privacy and data utility requests and a same user has dynamic requests for those two aspects under different scenarioes, a personalized trajectory privacy protection model will be proposed .

本项目对基于位置服务中的位置和轨迹隐私保护开展研究。针对位置隐私保护，首先拟采用博弈论的方法对位置隐私和服务质量进行权衡，以此指导位置隐私保护方案的设计；其次设计专门的加密方法，以此为基础在加密数据库上进行安全、高效的范围查询和k最近邻查询，最终实现云计算环境下基于位置服务中的位置隐私保护；然后对群组最近邻查询中位置隐私保护方案开展研究，使得群组用户在不泄露自己位置信息的情况下能够计算出距离群组用户最近的点，获得较准确的位置服务。针对轨迹隐私保护，我们拟提出一个位置感知的自适应轨迹k-匿名方法，用户根据对轨迹上不同点间隐私要求的差异，自适应地选择匿名集，在此基础上构造前后两个停留点间能够达到隐私保护最大化的k条轨迹；最后针对不同用户对轨迹隐私保护和数据利用率不同的需求，以及相同用户在不同场景下对两者动态变化的需求，提出个性化的轨迹隐私保护模型。