Purpose For decades, literature has reported on the perceived conflict between usability and
security. This mutual trade-off needs to be considered and addressed whenever security …

Across academia, government, and industry, data stewards are facing increasing pressure
to make datasets more openly accessible for researchers while also protecting the privacy of …

A private data federation enables clients to query the union of data from multiple data
providers without revealing any extra private information to the client or any other data …

Homomorphic encryption, secure multi-party computation, and differential privacy are part of
an emerging class of Privacy Enhancing Technologies which share a common promise: to …

Data analytics is a core part of modern decision making, especially in public policy.
However, there exists a tension between data privacy and otherwise socially beneficial …

Secure multi-party computation (MPC) allows a set of parties to compute a function jointly
while keeping their inputs private. MPC has been actively studied, and there are many …

Multiparty garbling is the most popular approach for constant-round secure multiparty
computation (MPC). Despite being the focus of significant research effort, instantiating prior …

This work examines privacy laws and regulations that limit disclosure of personal data, and
explores whether and how these restrictions apply when participants use cryptographically …

Among secure multi-party computation protocols, linear secret sharing schemes often do not
rely on cryptographic assumptions and are among the most straightforward to explain and to …

Extending knowledge by identifying and investigating valuable research questions and
problems is a core function of research. Research publications often suggest avenues for …