关键词: 自适应性学习模型, 位置服务（LBS）, 隐私保护, k-匿名

Abstract: Personalized k-anonymous models provide privacy protection based on user privacy preferences, giving users more control of location privacy, but due to the complexity of setting privacy preferences, even the most privacy-conscious users may ignore some of the problems. The purpose of this paper is to develop a framework to help users choose their own privacy preferences, effective management and access from the anonymous privacy content. Analyze a set of options that affect privacy configuration and build an adaptive learning model to help users make the right decisions and protect their privacy information. As the learning model matures, it will manage the user’s different privacy preferences in a variety of situations with minimal user intervention, prevent privacy disclosure, and encourage users to use the model’s recommended privacy settings.

Key words: adaptive learning model, location service（LBS）, privacy protection, k-anonymous