Combinatorial optimization
CRYPTO '99 Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology
Dynamic Flexible Constraint Satisfaction and Its Application to Al Planning
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CarTel: a distributed mobile sensor computing system
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Artificial Intelligence
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SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
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SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
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Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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Proceedings of the 8th international conference on Mobile systems, applications, and services
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Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Supporting Cooperative Caching in Disruption Tolerant Networks
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
A framework for context-aware privacy of sensor data on mobile systems
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
Proceedings of the 22nd international conference on World Wide Web companion
Exploiting innocuous activity for correlating users across sites
Proceedings of the 22nd international conference on World Wide Web
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Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user's privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of location traces can be deanonymized given an easily obtained social network graph. The key idea of our approach is that a user may be identified by those she meets: a "contact graph" identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees' DBLP social network (78 nodes). Our experiments show that 80% of users are identified precisely, while only 8% are identified incorrectly, with the remainder mapped to a small set of users.