Tuning the Utility-Privacy Trade-Off in Trajectory Data

Abstract

Trajectory data, often collected on a large scale with mobile sensors in smartphones and vehicles, are a valuable source for realiz-ing smart city applications, or for improving the user experience in mobile apps. But such data can also leak private information, such as a person’s whereabouts and their points of interest (POI). These in turn can reveal sensitive information, for example a person’s age, gender, religion, or home and work address. Location privacy preserving mechanisms (LPPM) can mitigate this issue by transforming data so that private details are protected. But privacy-preservation typically comes at the cost of a loss of utility. It can be challenging to find a suitable mechanism and the right settings to satisfy privacy as well as utility. In this work, we present Privacy Tuna, an interactive open-source framework to visualize trajectory data, and intuitively estimate data utility and privacy while applying various LPPMs. Our tool makes it easy for data owners to investigate the value of their data, choose a suitable privacy-preserving mechanism and tune its parameters to achieve a good utility-privacy trade-off.