Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form often contain sensitive attributes and publishing such data will leak individuals' privacy. Especially, a privacy threat occurs when an attacker can link a record to a specific individual based on some known partial information. Therefore, maintaining privacy in the published data is a critical problem. To prevent record linkage, attribute linkage, and similarity attacks based on the background knowledge of trajectory data, we propose a data privacy preservation with enhanced l-diversity. First, we determine those critical spatial-temporal sequences which are more likely to cause privacy leakage. Then, we perturb these sequences by adding or deleting some spatial-temporal points while ensuring the published data satisfy our (L, alpha, beta)-privacy, an enhanced privacy model from l-diversity. Our experiments on both synthetic and real-life datasets suggest that our proposed scheme can achieve better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

Automated summary

The widespread use of positioning technology has enabled the collection of people's movements for knowledge-based decision-making. However, the sensitive nature of the collected data raises privacy concerns. To address this, a data privacy preservation scheme with enhanced l-diversity is proposed to prevent privacy leakage. Experimental results show that this scheme can achieve better privacy without sacrificing utility compared to existing privacy preservation schemes on trajectory data.