Collaborative Research: SaTC: CORE: Medium: Differentially Private SQL with flexible privacy modeling, machine-checked system design, and accuracy optimization

协作研究：SaTC：核心：中：具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL

• 批准号: 2317233
• 负责人: Zeyu Ding
• 金额: $ 33.56万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317233&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Medium

Companies and government agencies maintain large databases crucial to their operations. Such databases contain sensitive information about people's interactions with state and local agencies (e.g., tax filings, travel data) or interactions with companies (e.g., customer profiles and purchase histories, employee salary and tax data, and performance reviews). However, such databases also have immense value for analytics that can be used to improve internal operations, guide policy decisions, and provide aggregate information about society. "Formal Privacy" is a scientific field that studies how to inject noise into analyses to protect confidential information without adversely affecting the utility of the analyses. However, existing technology is difficult to apply and requires significant technical expertise. The goal, and broader significance and importance of this project are to democratize access to advanced formal privacy tools. The project's novelties are (1) a customizable privacy model for capturing different privacy concerns in a database and (2) automated tools that reason about how much noise must be injected into a data analysis to satisfy these confidentiality concerns without adversely affecting the analysis results. Prior work used simple, pre-specified privacy models that severely limited the types of applications that can be supported and required significant technical expertise in the design of those systems to obtain accurate query answers. The project team develops a middleware application for SQL databases consisting of (1) automated tools for analyzing a database schema and interactively developing a privacy model of which data elements need the plausible deniability of differential privacy variations and (2) automated tools for reasoning about SQL queries and customize privacy-preserving query execution plans to the privacy model that is most appropriate for the data. The end result is an open-source, customizable, privacy-preserving database analytics system compatible with existing SQL databases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

公司和政府机构维护着对其运营至关重要的大型数据库。这些数据库包含有关人们与州和地方机构互动的敏感信息（例如，税务申报、旅行数据）或与公司的交互（例如，客户档案和购买历史、员工工资和税务数据以及绩效评估）。然而，这些数据库也具有巨大的分析价值，可用于改善内部运营，指导政策决策，并提供有关社会的汇总信息。“形式隐私”是一个科学领域，研究如何在分析中注入噪音，以保护机密信息，而不会对分析的效用产生不利影响。然而，现有技术很难应用，需要大量的技术专长。该项目的目标，以及更广泛的意义和重要性是使高级正式隐私工具的访问民主化。该项目的创新之处在于：（1）一个可定制的隐私模型，用于捕获数据库中的不同隐私问题;（2）自动化工具，用于推理必须向数据分析中注入多少噪声，以满足这些保密性问题，而不会对分析结果产生不利影响。 以前的工作使用简单的，预先指定的隐私模型，严重限制了可以支持的应用程序的类型，并需要在这些系统的设计，以获得准确的查询答案的重要技术专长。该项目团队开发了一个用于SQL数据库的中间件应用程序，包括（1）用于分析数据库模式和交互式开发隐私模型的自动化工具，其中数据元素需要差异隐私变化的合理可否认性，以及（2）用于推理SQL查询的自动化工具，并将隐私保护查询执行计划定制为最适合数据的隐私模型。 最终的结果是一个开源的、可定制的、隐私保护的数据库分析系统，与现有的SQL数据库兼容。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。