A demonstration of VisDPT
Xi Qin He, Nisarg Raval, Ashwin Machanavajjhala · Proceedings of the VLDB Endowment · 2016
The release of detailed taxi trips has motivated numerous useful studies, but has also triggered multiple privacy attacks on individuals' trips. Despite these attacks, no tools are available for systematically analyzing the privacy risk of released trajectory data. While, recent studies have proposed mechanisms to publish synthetic mobility data with provable privacy guarantees, the questions on -- 1) how to explain the theoretical privacy guarantee to non-privacy experts; and 2) how well private data preserves the properties of ground truth, remain unclear. To address these issues, we propose a system --- VisDPT that provides rich visualization of sensitive information in trajectory databases and helps data curators understand the impact on utility due to privacy preserving mechanisms. We believe VisDPT will enable data curators to take informed decisions while publishing sanitized data.