具有多样性约束的多样化分析数据
Diversifying Anonymized Data with Diversity Constraints
摘要：
最近推出的隐私立法旨在限制和控制公司发布和共享给第三方的个人数据量。这些真实数据中的大部分不仅是需要匿名的敏感数据，而且还包含来自各种个人的特征细节。这种多样性在从Web搜索到药物和产品开发的许多应用中都是理想的。不幸的是，数据匿名化技术在很大程度上忽略了其发布结果的多样性。这在随后的数据分析中无意中传播了潜在的偏见。我们研究的问题，找到一个多样化的匿名数据实例，多样性是通过一组多样性约束。我们形式化多样性约束，并研究其基础，如蕴涵和可满足性。我们表明，确定存在一个不同的，匿名的实例可以在PTIME，我们提出了一个基于聚类的算法。我们使用真实和合成数据进行了广泛的实验，显示了我们的技术的有效性，并改善了现有的基线。我们的工作符合最近的趋势，通过将多样性与隐私保护数据发布相结合来实现负责任的数据科学。
英文摘要：
Recently introduced privacy legislation has aimed to restrict and control the amount of personal data published by companies and shared to third parties. Much of this real data is not only sensitive requiring anonymization, but also contains characteristic details from a variety of individuals. This diversity is desirable in many applications ranging from Web search to drug and product development. Unfortunately, data anonymization techniques have largely ignored diversity in its published result. This inadvertently propagates underlying bias in subsequent data analysis. We study the problem of finding a diverse anonymized data instance where diversity is measured via a set of diversity constraints. We formalize diversity constraints and study their foundations such as implication and satisfiability. We show that determining the existence of a diverse, anonymized instance can be done in PTIME, and we present a clustering-based algorithm. We conduct extensive experiments using real and synthetic data showing the effectiveness of our techniques, and improvement over existing baselines. Our work aligns with recent trends towards responsible data science by coupling diversity with privacy-preserving data publishing.
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