Applications today rely on cloud databases for storing and querying time-series data. While
outsourcing storage is convenient, this data is often sensitive, making data breaches a …

Many organizations need large amounts of high quality data for their applications, and one
way to acquire such data is to combine datasets from multiple parties. Since these …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …

Many organizations stand to benefit from pooling their data together in order to draw
mutually beneficial insights—eg, for fraud detection across banks, better medical studies …

Background Data sharing is considered a crucial part of modern medical research.
Unfortunately, despite its advantages, it often faces obstacles, especially data privacy …

Data isolation has become an obstacle to scale up query processing over big data, since
sharing raw data among data owners is often prohibitive due to security concerns. A …

Recent years have witnessed the rapid development of the encrypted database, due to the
increasing number of data privacy breaches and the corresponding laws and regulations …

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 …

For healthcare datasets, it is often impossible to combine data samples from multiple sites
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …

There has been a recent effort in applying differential privacy on memory access patterns to
enhance data privacy. This is called differential obliviousness. Differential obliviousness is a …