Privacy Preservation for Trajectory Data Publishing by Look-Up Table Generalization

With the increasing of location-aware devices, it is easy to collect the trajectory of a person which can be represented as a sequence of visited locations with regard to timestamps. For some applications such as traffic management and location-based advertising, the trajectory data may need to be published with other private information. However, revealing the private trajectory and sensitive information of user poses privacy concerns especially when an adversary has the background knowledge of target user, i.e., partial trajectory information. In general, data transformation is needed to ensure privacy preservation before data releasing. Not only the privacy has to be preserved, but also the data quality issue must be addressed, i.e., the impact on data quality after the transformation should be minimized. LKC-privacy model is a well-known model to anonymize the trajectory data that are published with the sensitive information. However, computing the optimal LKC-privacy solution on trajectory data by the brute-force (BF) algorithm with full-domain generalization technique is highly time-consuming. In this paper, we propose a look-up table brute-force (LT-BF) algorithm to preserve privacy and maintain the data quality based on LKC-privacy model in the scenarios which the generalization technique is applied to anonymize the trajectory data efficiently. Subsequently, our proposed algorithm is evaluated with experiments. The results demonstrate that our proposed algorithm is not only returns the optimal solution as the BF algorithm, but also it is highly efficient.