Inference Attacks on Location Tracks

Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects’ home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, noise, and rounding. We show how much obscuration is necessary to maintain the privacy of all the subjects.

• Type: Conference
• Year: 2007
• Where: PERVASIVE
• Authors: John Krumm
• Keywords: Applied Computing, Location Data, PERVASIVE 2007, Privacy, Privacy Threats