Privacy and security have rapidly emerged as priorities in system design. One powerful
solution for providing both is privacy-preserving computation, where functions are computed …

Measuring people's interactions that span multiple websites can provide unique insight that
enables better products and improves people's experiences, but directly observing people's …

Private matching for compute (PMC) establishes a match between two datasets owned by
mutually distrusted parties (C and P) and allows the parties to input more data for the …

As companies work to provide the best possible experience for members, users, and
customers, it is crucial to understand how different people–particularly individuals from …

In this work, we focus on the Private Intersection-Sum (PIS) with cardinality problem: two
parties hold datasets containing user identifiers, and the second party additionally has an …

Clinical trials are crucial in the development of new medical treatment methods. To ensure
the correctness of clinical trial results, medical institutes need to collect and process large …

We extend two-party private set union for secure computation, by considering matching
between records having multiple identifiers (or keys), for example email and phone. In the …

Differentially private noise mechanisms commonly use symmetric noise distributions. This is
attractive both for achieving the differential privacy definition, and for unbiased expectations …

The ratio of two Gaussians is useful in many contexts of statistical inference. We discuss
statistically valid inference of the ratio under Differential Privacy (DP). We use the delta …