Designing a Location Trace Anonymization Contest
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For a better understanding of anonymization methods for location traces, we have designed and held a location trace anonymization contest. Our contest deals with a long trace (400 events per user) and fine-grained locations (1024 regions). In our contest, each team anonymizes her original traces, and then the other teams perform privacy attacks against the anonymized traces in a partial-knowledge attacker model where the adversary does not know the original traces. To realize such a contest, we propose a location synthesizer that has diversity and utility; the synthesizer generates different synthetic traces for each team while preserving various statistical features of real traces. We also show that re-identification alone is insufficient as a privacy risk and that trace inference should be added as an additional risk. Specifically, we show an example of anonymization that is perfectly secure against re-identification and is not secure against trace inference. Based on this, our contest evaluates both the re-identification risk and trace inference risk and analyzes their relationship. Through our contest, we show several findings in a situation where both defense and attack compete together. In particular, we show that an anonymization method secure against trace inference is also secure against re-identification under the presence of appropriate pseudonymization.
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