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对其运营至关重要的大型数据库。此类数据库包含有关人们与州和地方机构（例如税务文件，旅行数据）的互动的敏感信息或与公司（例如，客户资料和购买历史，员工薪金和税收数据以及绩效审查）的互动。但是，这些数据库对于分析也具有巨大的价值，可用于改善内部运作，指导政策决策并提供有关社会的汇总信息。 “正式隐私”是一个科学领域，它研究如何将噪声注入分析中以保护机密信息，而不会对分析的实用性产生不利影响。但是，现有技术很难应用，需要大量的技术专业知识。该项目的目标以及更广泛的意义和重要性是使获得高级正式隐私工具的访问权限。该项目的新颖性是（1）一种可自定义的隐私模型，用于在数据库中捕获不同的隐私问题和（2）自动化工具，这些工具认为必须将多少噪声注入数据分析中，以满足这些机密性问题而不会对分析结果产生不利影响。 先前的工作使用了简单的，预先指定的隐私模型，这些模型严重限制了可以支持的应用程序的类型，并且在这些系统的设计中需要具有重要的技术专业知识，以获得准确的查询答案。项目团队为SQL数据库开发了一个中间件应用程序，该应用程序由（1）自动化工具组成，用于分析数据库架构并交互式开发一个隐私模型，该模型需要该模型，该模型需要该模型，该模型需要对差异隐私变化的合理可否认性以及（2）自动化工具，以对SQL查询和自定义质量验证的数据进行推理，以确定质量范围的数据，以确定数据，以符合该模型的模型。 最终结果是一个与现有SQL数据库兼容的开源，可自定义的，可自定义的，隐私的数据库分析系统。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。