Generalizing data to provide anonymity when disclosing information (abstract)
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Recently, trajectory data mining has received a lot of attention in both the industry and the academic research. In this paper, we study the privacy threats in trajectory data publishing and show that traditional anonymization methods are not applicable for trajectory data due to its challenging properties: high-dimensional, sparse, and sequential. Our primary contributions are (1) to propose a new privacy model called LKC-privacy that overcomes these challenges, and (2) to develop an efficient anonymization algorithm to achieve LKC-privacy while preserving the information utility for trajectory pattern mining.