Large-scale collection of contextual information is often essential in order to gather statistics,
train machine learning models, and extract knowledge from data. The ability to do so in a …

In this paper we propose a new approach to privately compute the set-union cardinality and
the set-intersection cardinality among multiple honest-but-curious parties. Our approach is …

Cardinality estimators like HyperLogLog are sketching algorithms that estimate the number
of distinct elements in a large multiset. Their use in privacy-sensitive contexts raises the …

We consider a scenario where multiple organizations holding large amounts of sensitive
data from their users wish to compute aggregate statistics on this data while protecting the …

Private set intersection cardinality (PSI-CA) allows two parties, the sender and receiver, to
compute the cardinality of the intersection, without revealing anything more to the other …

The computation of private set union/intersection cardinality (PSU-CA/PSI-CA) is one of the
most intensively studied problems in privacy preserving data mining (PPDM). However, the …

Secure computation is a promising privacy enhancing technology, but it is often not scalable
enough for data intensive applications. On the other hand, the use of sketches has gained …

Recent years have seen a strong uptick in both the prevalence and real-world
consequences of false information spread through online platforms. At the same time …

Private set-intersection (PSI) allows a client to only learn the intersection between his/her set
C and the set S of another party, while this latter party learns nothing. We aim to enhance …

Purpose In the digital age, organizations want to build a more powerful machine learning
model that can serve the increasing needs of people. However, enhancing privacy and data …