(C, K)m-Anonymity: A Model to Resist Sub-trajectory Linkage Attacks
Guo Hui, Han Jianmin, Jianfeng Lu, Hao Ming Peng, Tang Changbin · 2015
(k, l)m-anonymity is an effective model to preserve privacy for trajectory data publishing. However, the model cannot prevent sensitive location disclosure on the condition that l is large. To solve the problem, this paper proposes a (c, k)m-anonymity model, which can prevent the disclosure of identity and sensitive location information. We also propose an algorithm to realize (c, k)m-anonymity. Experiments show that the proposed algorithm can generate anonymous trajectories with high data utility.