The growth of the Internet has triggered tremendous opportunities for cooperative
computation, where people are jointly conducting computation tasks based on the private …

Abstract Secure Multi-party Computation (SMPC) is a family of protocols which allow some
parties to compute a function on their private inputs, obtaining the output at the end and …

Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the
client and the server and is a promising technique in the machine-learning-as-a-service …

Important decisions about people are increasingly made by algorithms: Votes are counted;
voter rolls are purged; financial aid decisions are made; taxpayers are chosen for audits; air …

Neural Networks (NN) are today increasingly used in Machine Learning where they have
become deeper and deeper to accurately model or classify high-level abstractions of data …

We describe a novel approach for two-party private set intersection (PSI) with semi-honest
security. Compared to existing PSI protocols, ours has a more favorable balance between …

Many recent private set intersection (PSI) protocols encode input sets as polynomials. We
consider the more general notion of an oblivious key-value store (OKVS), which is a data …

We present a new protocol for two-party private set intersection (PSI) with semi-honest
security in the plain model and one-sided malicious security in the random oracle model …

We study the problem of searching on data that is encrypted using a public key system.
Consider user Bob who sends email to user Alice encrypted under Alice's public key. An …

We consider the problem of computing the intersection of private datasets of two parties,
where the datasets contain lists of elements taken from a large domain. This problem has …