Private Data Exploration with Provable Guarantees

计算机技术使我们个人生活的大量数字痕迹能够被收集和存储。许多研究项目和现实生活中的应用程序都依赖于对这些数据集的探索，但最近欧洲通过的《通用数据保护条例》（GDPR）和备受争议的Facebook隐私丑闻证明了这一点。一个简单的临时隐私保护方法可能会给个人和公司带来巨大的成本。现有的数据库系统具有强大的隐私保证，如差分隐私，对分析师真正关心的问题没有提供直接的最佳保证，包括查询答案中的错误（准确性）和接收答案之前的等待时间（延迟）。由于没有足够的隐私背景，分析师在使用这些系统时很容易遭受准确性差或等待时间长的问题。将现有的数据探索技术（如采样或在线聚合）简单地应用于私有设置可能会破坏隐私保证或导致准确性差。此外，这些系统提供相同的隐私保证，不能满足不同应用程序的不同隐私要求。 我提出的研究主要旨在通过可证明的保证实现私人数据探索，这样所有参与方都不需要是隐私专家-数据分析师可以自然地使用丰富的查询模板和功能来探索敏感数据，而数据管理员可以轻松地跟踪整个探索过程中的隐私损失。这项研究计划将建立在我目前的工作定制隐私和私人探索系统。为此，我的研究计划包括一系列短期和长期研究任务。短期研究计划旨在（1）在最先进的隐私保障，差异隐私下开发准确感知的数据探索，以及（2）实现私人采样和在线聚合，以增强数据探索期间的交互式用户体验。长期研究计划是在私有数据探索中支持除差异隐私之外的可定制隐私保证。拟议的框架将通过开放源码数据库系统的原型进行测试。

Computing technology has enabled massive digital traces of our personal lives to be collected and stored. Numerous research projects and real-life applications crucially rely on the exploration of these datasets, but there are great privacy concerns as demonstrated by the recent passing of the General Data Protection Regulation (GDPR) in Europe and the controversial Facebook privacy scandal. A simple ad-hoc privacy-preserving approach can lead to a drastic cost to individuals and companies. Existing database systems with strong privacy guarantee such as differential privacy provide no direct, optimal guarantees for what the analysts really care, including the error in the query answer (accuracy) and the waiting time before receiving the answer (latency). With insufficient privacy background, the analysts can easily suffer poor accuracy or long waiting time when using these systems. Naively applying existing data exploration techniques, such as sampling or online aggregation, to the private setting can either break the privacy guarantee or result in poor accuracy. Moreover, these systems offer the same privacy guarantee and cannot meet the diverse privacy requirements of different applications. My proposed research aims primarily at enabling private data exploration with provable guarantees such that all parties involved are not required to be privacy experts -- data analysts can naturally explore sensitive data with a rich set of query templates and functionalities while data curator can easily track the privacy loss over the entire exploration process. This research program will build on my current work on customized privacy and private exploration systems. To do so, my research program includes a set of short-term and long-term research tasks. The short-term research plan aims to (1) develop accuracy-aware data explorations under the state-of-the-art privacy guarantee, differential privacy, and (2) enable private sampling and online aggregation to enhance the interactive user experience during data exploration. The long-term research plan is to support customizable privacy guarantees other than differential privacy in private data exploration. The proposed frameworks will be tested through prototyping with open-sourced database systems.