PrivLava：在差分隐私下合成带有外键的关系数据
PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy
AI总结：
针对多关系数据合成中的隐私保护与外键相关性建模难题，提出首个在差分隐私下合成带外键关系数据的解决方案 PrivLava，利用图模型和潜在变量捕捉关系间相关性，显著提升了聚合查询准确性。
摘要：
在保护隐私的同时回答数据库查询是一个重要问题，近年来引起了大量研究关注。解决该问题的一种经典方法是使用合成数据。即，我们用合成数据库 R* 替换输入数据库 R，该合成数据库 R* 保留了 R 的特征，并使用 R* 来回答查询。然而，现有的关系数据合成解决方案要么无法提供强大的隐私保护，要么假设 R 仅包含单一关系。此外，将现有的单关系解决方案扩展到多关系的情况具有挑战性，因为它们无法建模由外键引起的复杂相关性。因此，具有强隐私保证的多关系数据合成是一个开放问题。在本文中，我们通过提出 PrivLava 解决了上述开放问题，这是首个在差分隐私下合成带有外键的关系数据的解决方案。差分隐私是学术界和工业界广泛采用的严格隐私框架。PrivLava 的核心思想是使用图模型对 R 中的数据分布进行建模，并引入潜在变量来捕捉由外键引起的关系间相关性。我们证明 PrivLava 支持构成有向无环图的任意外键引用，并且能够处理 R 包含公共和私有关系混合的常见情况。在人口普查数据集和 TPC-H 基准上的大量实验表明，PrivLava 在合成数据上处理的聚合查询的准确性方面显著优于其竞争对手。
英文摘要：
Answering database queries while preserving privacy is an important problem that has attracted considerable research attention in recent years. A canonical approach to this problem is to use synthetic data. That is, we replace the input database R with a synthetic database R* that preserves the characteristics of R, and use R* to answer queries. Existing solutions for relational data synthesis, however, either fail to provide strong privacy protection, or assume that R contains a single relation. In addition, it is challenging to extend the existing single-relation solutions to the case of multiple relations, because they are unable to model the complex correlations induced by the foreign keys. Therefore, multi-relational data synthesis with strong privacy guarantees is an open problem. In this paper, we address the above open problem by proposing PrivLava, the first solution for synthesizing relational data with foreign keys under differential privacy, a rigorous privacy framework widely adopted in both academia and industry. The key idea of PrivLava is to model the data distribution in R using graphical models, with latent variables included to capture the inter-relational correlations caused by foreign keys. We show that PrivLava supports arbitrary foreign key references that form a directed acyclic graph, and is able to tackle the common case when R contains a mixture of public and private relations. Extensive experiments on census data sets and the TPC-H benchmark demonstrate that PrivLava significantly outperforms its competitors in terms of the accuracy of aggregate queries processed on the synthetic data.
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