Elements of information theory
Elements of information theory
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Model T: an empirical model for user registration patterns in a campus wireless LAN
Proceedings of the 11th annual international conference on Mobile computing and networking
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Power law and exponential decay of inter contact times between mobile devices
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Preserving privacy in gps traces via uncertainty-aware path cloaking
Proceedings of the 14th ACM conference on Computer and communications security
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Hiding stars with fireworks: location privacy through camouflage
Proceedings of the 15th annual international conference on Mobile computing and networking
Short paper: the NetSANI framework for analysis and fine-tuning of network trace sanitization
Proceedings of the fourth ACM conference on Wireless network security
Automatic inference of movements from contact histories
Proceedings of the ACM SIGCOMM 2011 conference
Anonymization of location data does not work: a large-scale measurement study
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Trajectory privacy in location-based services and data publication
ACM SIGKDD Explorations Newsletter
Quantifying location privacy: the case of sporadic location exposure
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Assessing location privacy in mobile communication networks
ISC'11 Proceedings of the 14th international conference on Information security
The impact of trace and adversary models on location privacy provided by K-anonymity
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
Evaluating the privacy risk of location-based services
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Location privacy in relation to trusted peers
STM'11 Proceedings of the 7th international conference on Security and Trust Management
Inferring human mobility patterns from anonymized mobile communication usage
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
ACM SIGMOBILE Mobile Computing and Communications Review
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Mobility traces of people and vehicles have been collected and published to assist the design and evaluation of mobilee networks, such as large-scale urban sensing networks. Although the published traces are often made anonymous in that the true identities of nodes are replaced by random identifiers, the privacy concern remains. This is because in real life, nodes are open to observations in public spaces, or they may voluntarily or inadvertently disclose partial knowledge of their whereabouts. Thus, snapshots of nodes' location information can be learned by interested third parties, e.g., directly through chance/engineered meetings between the nodes and their observers, or indirectly through casual conversations or other information sources about people. In this paper, we investigate how an adversary, when equipped with a small amount of the snapshot information termed as side information, can infer an extended view of the whereabouts of a victim node appearing in an anonymous trace. Our results quantify the loss of victim nodes' privacy as a function of the nodal mobility (captured in both real and synthetic traces), the inference strategies of adversaries, and any noise that may appear in the trace or the side information. Generally, our results indicate that the privacy concern is significant in that a relatively small amount of side information is sufficient for the adversary to infer the true identity (either uniquely or with high probability) of a victim in a set of anonymous traces.