Privacy-Enhancing Technologies (PETs) are foundational for a future where data can be used without compromising privacy. While the community has largely focused on advancing the cryptographic foundations of PETs, real-world security of PETs is threatened by the very software systems designed to make them accessible, including PET-oriented compilers and frameworks.

The goal of my research is to ensure that the practical systems supporting PETs are dependable. In this talk, I will present my work on developing novel, automated techniques to systematically uncover critical vulnerabilities in the software systems of PETs. I will show two thrusts of my research: (1) automatically discovering severe logic bugs in domain-specific compilers for PETs, and (2) identifying and mitigating new, subtle security risks in PET-enhanced machine learning frameworks. The tools from this research have uncovered dozens of bugs (some with high security impact) in high-stakes PET systems and have been adopted by leading PET industry users. I will conclude by discussing my future research vision towards building provably dependable PET ecosystems.