DPXPlain：私下解释聚合查询答案
DPXPlain: Privately Explaining Aggregate Query Answers
摘要：
差分隐私（Differential Privacy，DP）是一种最新的、严格的隐私概念，用于回答聚合数据库查询，同时保护数据中敏感信息的隐私。然而，在当今的数据分析时代，它为用户理解查询结果中观察到的趋势和异常提出了新的挑战：意外的答案是由于数据本身，还是由于必须添加以保留DP的额外噪声？在第二种情况下，甚至用户对查询结果的观察也可能是错误的。在第一种情况下，我们还能从敏感数据中挖掘出有趣的解释，同时保护其隐私吗？为了解决这些挑战，我们提出了一个三阶段的框架DPXPlain，这是第一个系统，以我们所知的解释组聚合查询答案DP。在它的三个阶段中，DPXPlain（a）用DP回答逐组聚合查询，（b）允许用户比较两组的聚合值，并且以高概率评估该比较是否成立或被DP噪声翻转，以及（c）最终提供包含近似"前k"解释谓词以及它们的相对影响和以置信区间的形式的排名的解释表，同时保证所有步骤中的DP。我们对DPXPlain进行了广泛的实验分析，在真实和合成数据上使用多个用例，表明DPXPlain有效地提供了具有良好准确性和实用性的有见地的解释。
英文摘要：
Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in the data. In today's era of data analysis, however, it poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? In the second case, even the observation made by the users on query results may be wrong. In the first case, can we still mine interesting explanations from the sensitive data while protecting its privacy? To address these challenges, we present a three-phase framework DPXPlain, which is the first system to the best of our knowledge for explaining group-by aggregate query answers with DP. In its three phases, DPXPlain (a) answers a group-by aggregate query with DP, (b) allows users to compare aggregate values of two groups and with high probability assesses whether this comparison holds or is flipped by the DP noise, and (c) eventually provides an explanation table containing the approximately `top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps. We perform an extensive experimental analysis of DPXPlain with multiple use-cases on real and synthetic data showing that DPXPlain efficiently provides insightful explanations with good accuracy and utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
