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Conference Papers Year : 2016

Testing the robustness of anonymization techniques: acceptable versus unacceptable inferences

Abstract

Anonymization is a critical issue because data protection regulations such as the European Direc- tive 95/46/EC and the European General Data Protection Regulation (GDPR) explicitly exclude from their scope \anonymous information" and \personal data rendered anonymous"1. However, turning this general statement into effective criteria is not an easy task. In order to facilitate the implementation of this provision, the Working Party 29 (WP29) has published in April 2014 an Opinion on Anonymization Techniques2. This Opinion puts forward three criteria correspond- ing to three risks called respectively "singling out", "linkability" and "inference". In this paper, we first discuss these criteria and suggest that they are neither necessary nor effective to decide upon the robustness of an anonymization algorithm (Section 2). Then we propose an alternative approach relying on the notions of acceptable versus unacceptable inferences (Section 3) and we introduce differential testing, a practical way to implement this approach using machine learning techniques (Section 4). The last section discusses related work and suggests avenues for future research (Section 5).
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Dates and versions

hal-01399858 , version 1 (21-11-2016)

Identifiers

  • HAL Id : hal-01399858 , version 1

Cite

Gergely Acs, Claude Castelluccia, Daniel Le Métayer. Testing the robustness of anonymization techniques: acceptable versus unacceptable inferences. The Brussels Privacy Symposium , Nov 2016, brussels, Belgium. ⟨hal-01399858⟩
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