Private Data Exploration with Provable Guarantees

具有可证明保证的私人数据探索

• 批准号: RGPIN-2019-04770
• 负责人: He, Xi
• 金额: $ 1.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750198
• 关键词: Private; Data; Exploration; Provable; Guarantees

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.

计算技术使我们个人生活的大量数字痕迹得以收集和存储。许多研究项目和现实生活中的应用都严重依赖于对这些数据集的探索，但欧洲最近通过的《通用数据保护条例》(GDPR) 和备受争议的 Facebook 隐私丑闻证明了人们对隐私的担忧。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。现有的具有强大隐私保证（例如差分隐私）的数据库系统并没有为分析人员真正关心的内容提供直接的、最优的保证，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）。由于隐私背景不足，分析人员在使用这些系统时很容易遭遇准确性差或等待时间长的问题。天真地将现有的数据探索技术（例如采样或在线聚合）应用于私人环境可能会破坏隐私保证或导致准确性较差。而且这些系统提供的隐私保障相同，无法满足不同应用的多样化隐私需求。 我提出的研究主要旨在通过可证明的保证来实现私有数据探索，这样所有相关方都不需要成为隐私专家——数据分析师可以自然地通过一组丰富的查询模板和功能来探索敏感数据，而数据管理者可以轻松地跟踪敏感数据。整个探索过程中的隐私损失。该研究计划将建立在我目前关于定制隐私和私人探索系统的工作的基础上。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）在最先进的隐私保证、差异隐私下开发准确性感知的数据探索，以及（2）实现隐私采样和在线聚合，以增强数据过程中的交互式用户体验勘探。长期研究计划是支持私人数据探索中除差异隐私之外的可定制隐私保证。拟议的框架将通过开源数据库系统的原型设计进行测试。