Current solutions for privacy-preserving data sharing among multiple parties either depend
on a centralized authority that must be trusted and provides only weakest-link security (eg …

Many existing privacy-preserving techniques for querying distributed databases of sensitive
information do not scale for large databases due to the use of heavyweight cryptographic …

People and machines are collecting data at an unprecedented rate. Despite this newfound
abundance of data, progress has been slow in sharing it for open science, business, and …

Gathering and processing sensitive data is a difficult task. In fact, there is no common recipe
for building the necessary information systems. In this paper, we present a provably secure …

Legal and ethical restrictions on accessing relevant data inhibit data science research in
critical domains such as health, finance, and education. Synthetic data generation …

People and machines are recording data at an unprecedented rate. At the same time,
progress has been slow in making data available for open science and other research …

We present SECRECY, a system for privacy-preserving collaborative analytics as a service.
SECRECY allows multiple data holders to contribute their data towards a joint analysis in …

Computing aggregate statistics about user data is of vital importance for a variety of services
and systems, but this practice has been shown to seriously undermine the privacy of users …

This article is devoted to the novel situation, where a large distributed cloud database is a
union of several separate databases belonging to individual database owners who are not …

Recently, a number of systems have been deployed that gather sensitive statistics from user
devices while giving differential privacy guarantees. One prominent example is the …