Anti-discrimination Analysis Using Privacy Attack Strategies
Anti-discrimination Analysis Using Privacy Attack Strategies
复制标题
使用隐私攻击策略的反歧视分析
DOI：
10.1007/978-3-662-44851-9_44
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Xiangliang Zhang
中科院分区：
文献类型：
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作者：
S. Ruggieri;S. Hajian;F. Kamiran;Xiangliang Zhang
从数据中发现社会歧视是识别针对受法律保护群体（例如少数民族）的非法和不道德歧视模式的一项重要任务。我们将隐私攻击策略部署为硬假设下的歧视发现工具，这些假设在文献中很少涉及：间接歧视发现、隐私意识歧视发现和歧视数据恢复。这种直觉来自于上述三种情况中反歧视机构的角色与私人数据发布中攻击者的角色之间有趣的相似之处。我们设计基于 Frechet 边界攻击、属性推理攻击和极简攻击的策略和算法，目的是揭示隐藏的歧视行为。实验结果表明，它们可以成为反歧视当局手中的有效工具。
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 Frechet 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.