Title: Differentially Private Explanations for Clusters Show Chinese title

Title: 差分隐私的聚类解释

Abstract: The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving the privacy of data contributors. One of the fundamental tasks of data analysis is clustering , which is meant to unravel hidden patterns within complex datasets. However, interpreting clustering results poses significant challenges, and often necessitates an extensive analytical process. Interpreting clustering results under DP is even more challenging, as analysts are provided with noisy responses to queries, and longer, manual exploration sessions require additional noise to meet privacy constraints. While increasing attention has been given to clustering explanation frameworks that aim at assisting analysts by automatically uncovering the characteristics of each cluster, such frameworks may also disclose sensitive information within the dataset, leading to a breach in privacy. To address these challenges, we present DPClustX, a framework that provides explanations for black-box clustering results while satisfying DP. DPClustX takes as input the sensitive dataset alongside privately computed clustering labels, and outputs a global explanation, emphasizing prominent characteristics of each cluster while guaranteeing DP. We perform an extensive experimental analysis of DPClustX on real data, showing that it provides insightful and accurate explanations even under tight privacy constraints. Show Chinese abstract

Abstract: 保护敏感数据的迫切需求催生了各种隐私定义的版本。在这其中，差分隐私（DP）被认为是隐私概念中最严格和最安全的一种，它能够在保护数据贡献者隐私的同时进行数据分析。数据分析的一项基本任务就是聚类，旨在揭示复杂数据集中隐藏的模式。然而，解释聚类结果带来了显著的挑战，并且通常需要一个详尽的分析过程。在差分隐私下解释聚类结果更具挑战性，因为分析师得到的是查询的噪声响应，并且更长的手动探索会话需要额外的噪声以满足隐私约束。尽管越来越多的关注被放在旨在通过自动揭示每个聚类特征来帮助分析师的聚类解释框架上，但这些框架也可能泄露数据集中的敏感信息，从而导致隐私泄露。为了解决这些挑战，我们提出了DPClustX，这是一个在满足差分隐私的同时提供黑盒聚类结果解释的框架。DPClustX以敏感数据集及其私有计算的聚类标签作为输入，并输出全局解释，强调每个聚类的突出特征，同时确保满足差分隐私。我们在真实数据上对DPClustX进行了广泛的实验分析，结果显示即使在严格的隐私约束下，它也能提供具有洞察力且准确的解释。