Protecting against inference attacks on co-location data

The proliferation of location-centric applications results in massive amounts of individual location data that can benefit domains such as transportation, urban planning, etc. However, sensitive personal data can be derived from location datasets. In particular, co-location of users can disclose one's social connections, intimate partners, business associates, etc. We derive a powerful inference attack that makes extensive use of background knowledge in order to expose an individual's co-locations. We also show that existing techniques for location protection, which do not focus specifically on co-locations, distort data excessively, resulting in sanitized datasets with poor utility. We propose three privacy mechanisms that are customized for co-locations, and provide various trade-offs in terms of user privacy and data utility. Our extensive experimental evaluation on a real geo-social network dataset shows that the proposed approaches achieve good data utility and do a good job of protecting against discovery of co-locations, even when confronted with a powerful adversary.