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

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.