چكيده به لاتين
In this thesis, A Design for Location-Based Services with Emphasis on Preserving the privacy of Users, for protecting location privacy and query privacy is proposed. An iterative DBSCAN clustering method is developed to categorize the user’s requests as clusters for providing location anonymity. Meanwhile, the diversity method for preserving the query privacy is used to create clusters. Also, KD-tree is utilized diversity and improve the process of neighborhood search. The accuracy of users' location and the information of them is inversely proportional to the user privacy preserving degree K and is directly proportional to the quality of query service. In order to balance privacy preserving and query quality caused by the accuracy of location information, a clustering algorithm eliminates outlieress based on the K-anonymity location privacy preserving. The experimental results demonstrate that in proposed design improves on Numbers of Clusters, shorter CloaKing Time, higher Entropy, and Quality of Service.
