A Framework for Generating Network-Based Moving Objects
Geoinformatica
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IEEE Transactions on Knowledge and Data Engineering
Location Privacy in Pervasive Computing
IEEE Pervasive Computing
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Prediction and indexing of moving objects with unknown motion patterns
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VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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Proceedings of the 1st international conference on Mobile systems, applications and services
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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SPC'05 Proceedings of the Second international conference on Security in Pervasive Computing
Trajectory anonymity in publishing personal mobility data
ACM SIGKDD Explorations Newsletter
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This paper studies the problem of protecting individual privacy when continuously publishing a stream of location trace data collected from a population of users. Fundamentally, this leads to the new challenge of anonymizing data that evolves in predictable ways over time. Our main technical contribution is a novel formal framework for reasoning about privacy in this setting. We articulate a new privacy principle called temporal unlinkability. Then, by incorporating a probabilistic model of data change (in this case, user motion), we are able to quantify the risk of privacy violations. Within this framework, we develop an initial set of algorithms for continuous privacy-preserving publishing. Finally, our experiments demonstrate the shortcomings of previous publishing techniques that do not account for inference based on predictable data change, and they demonstrate the feasibility of the new approach.