Privacy-Preserving Release of Spatio-Temporal Density

Gergely Acs 1 Gergely Biczók 1 Claude Castelluccia 2
2 PRIVATICS - Privacy Models, Architectures and Tools for the Information Society
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : In today’s digital society, increasing amounts of contextually rich spatio-temporal information are collected and used, e.g., for knowledge-based decision making, research purposes, optimizing operational phases of city management, planning infrastructure networks, or developing timetables for public transportation with an increasingly autonomous vehicle fleet. At the same time, however, publishing or sharing spatio-temporal data, even in aggregated form, is not always viable owing to the danger of violating individuals’ privacy, along with the related legal and ethical repercussions. In this chapter, we review some fundamental approaches for anonymizing and releasing spatio-temporal density, i.e., the number of individuals visiting a given set of locations as a function of time. These approaches follow different privacy models providing different privacy guarantees as well as accuracy of the released anonymized data. We demonstrate some sanitization (anonymization) techniques with provable privacy guarantees by releasing the spatio-temporal density of Paris, in France. We conclude that, in order to achieve meaningful accuracy, the sanitization process has to be carefully customized to the application and public characteristics of the spatio-temporal data.
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https://hal.inria.fr/hal-01921891
Contributor : Claude Castelluccia <>
Submitted on : Wednesday, November 14, 2018 - 10:44:54 AM
Last modification on : Friday, June 21, 2019 - 9:49:28 AM

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Gergely Acs, Gergely Biczók, Claude Castelluccia. Privacy-Preserving Release of Spatio-Temporal Density. Handbook of Mobile Data Privacy, Springer, pp.307-335, 2018, 978-3-319-98160-4. ⟨10.1007/978-3-319-98161-1_12⟩. ⟨hal-01921891⟩

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