关键词: 隐私保护, 差异性, k-匿名, 轨迹数据发布

Abstract: Based on k-anonymity model, the traditional algorithm which protects mobile objects’trajectory data when they are publishing has a possibility of leaking the objects’personal privacy. To solve this problem, this thesis designs a new kind of algorithm which can protect trajectory data privacy when publishing. This algorithm is based on k-anonymity, divides the two-dimensional space into cells of equal size, defines the standard of l-diversity under trajectory, structures anonymous set via clustering under the premise of k-anonymity model and makes sure that the trajectories which gather anonymously meet the standard of l-diversity so as to minimize the risk of leaking user’s privacy that caused by the lack of diversity. The experimental results show that this algorithm is feasible and effective.

Key words: privacy preservation, diversity, k-anonymity, trajectory data publication