Anti-discrimination Analysis Using Privacy Attack Strategies

Salvatore Ruggieri, Sara Hajian, Faisal Kamiran, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.
Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
PublisherSpringer Nature
Number of pages17
ISBN (Print)9783662448502
StatePublished - Sep 1 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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