Abstract

Privacy is important both for individuals and corporations. While individuals want to keep their personally identifiable information private, corporations want to protect the privacy of their proprietary data in order not to lose their competitive advantage. The academic literature has extensively analyzed privacy from a theoretical perspective. We use these theoretical results to address the need for privacy in real-world applications, for both individuals and corporations. We focus on different variations of a cryptographic primitive from the literature: secure Multi-Party Computation (MPC). MPC helps different parties compute a joint function on their private inputs, without disclosing them. In this dissertation, we look at real-world applications of MPC, and aim to protect the privacy of personal and/or proprietary data. Our main aim is to match theory to practical applications. The first work we present in this dissertation is a blockchain-based, generic MPC system that can be used in applications where personal and/or proprietary data is involved. Then we present a system that performs privacy-preserving link prediction between two graph databases using private set intersection cardinality (PSI-CA). The next use case we present again uses PSI-CA to perform contact tracing in order to track the spread of a virus in a population. The last use case is a genomic test realized by one time programs. Finally, this dissertation provides a comparison of the different MPC techniques and a detailed discussion about this comparison.