We present Whisper, a system for privacy-preserving collection of aggregate statistics. Like
prior systems, a Whisper deployment consists of a small set of non-colluding servers; these …

The modern Internet is built on systems that incentivize collection of information about users.
In order to minimize privacy loss, it is desirable to prevent these systems from collecting …

This paper proposes POPSTAR, a new lightweight protocol for the private computation of
heavy hitters, also known as a private threshold reporting system. In such a protocol, the …

Federated learning (FL) has rapidly become a compelling paradigm that enables multiple
clients to jointly train a model by sharing only gradient updates for aggregation, without …

Recent advancements in transformers have revolutionized machine learning, forming the
core of Large language models (LLMs). However, integrating these systems into everyday …

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 …

Abstract Distributed Point Functions (DPFs) are a useful cryptographic primitive enabling a
dealer to distribute short keys to two parties, such that the keys encode additive secret …

Insight into user experience and behavior is critical to the success of large software systems
and web services. Gaining such insights, while preserving user privacy, is a significant …

A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to
classify its data using a tree model from a model owner (MO) and only reveals an inference …

Fully homomorphic encryption (FHE) has become progressively more viable in the years
since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in …