High-stakes applications, such as medicine and defense, increasingly rely on processing sensitive data held by multiple organizations. However, those organizations are often unwilling or unable to share their data due to privacy concerns and regulations, such as HIPAA and GDPR. For example, during COVID-19, the NIH needed statistics from hospitals to support public health analytics, but sharing patient records could expose personal information. Secure collaborative analytics (SCA) enables multiple mutually distrustful data providers to jointly answer SQL queries over the union of their private records without revealing them to anyone. Cryptographic techniques, such as zero-knowledge proofs (ZKPs) and secure multi-party computation (MPC), protect data privacy and verify the integrity of query answers in SCA, but applying them to query evaluation efficiently and at scale is not trivial.

My doctoral research builds database management systems with efficient, scalable, and trustworthy query evaluation for SCA. As my first project, ZKSQL lets a single data provider prove to a client with ZKPs that its query answers are correct and complete with respect to its committed private records. It verifies sorts and joins efficiently with multisets. Next, RESCU-SQL lets mutually distrustful computing parties work together to evaluate queries under MPC, ensuring malicious security against a dishonest majority, aided by a lightweight trusted coordinator to make the execution more efficient. To scale SCA to more data and providers, CASTLE marries these two approaches into a single workflow. First, each provider concurrently evaluates operations over its private records locally and proves the intermediate results to others with ZKPs. CASTLE then runs an authenticated bridge protocol to convert these results into certified inputs for joint evaluation under MPC. It realizes the local and joint phases with ZKSQL and RESCU-SQL. This framework guarantees end-to-end integrity from the committed source records to the final query answer.