Name
Title | ||
|---|---|---|
Sensitive Attribute Privacy Preservation Of Trajectory Data Publishing Based On L-Diversity |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10619-020-07318-7 | DISTRIBUTED AND PARALLEL DATABASES |
Keywords | DocType | Volume |
Sensitive attribute, Privacy preservation, Trajectory data publishing | Journal | 39 |
Issue | ISSN | Citations |
3 | 0926-8782 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |