Privacy-preserving Wi-Fi Analytics

Abstract : As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. Thus, while there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters called Pan-Private BLIP. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection and more generally t-out-of-n cardinalities). Finally, we evaluate expirementally our approach and compare it to previous ones on real datasets.
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Contributor : Mathieu Cunche <>
Submitted on : Wednesday, February 28, 2018 - 9:20:25 AM
Last modification on : Monday, November 19, 2018 - 9:16:10 PM

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Mohammad Alaggan, Mathieu Cunche, Sébastien Gambs. Privacy-preserving Wi-Fi Analytics. Proceedings on Privacy Enhancing Technologies, De Gruyter Open, 2018, 2018 (2), pp.4-26. ⟨10.1515/popets-2018-0010⟩. ⟨hal-01719211⟩



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