With the ever-increasing requirement of storage and computation resources, it is unrealistic
for local devices (with limited sources) to implement large-scale data processing. Therefore …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Private Set Intersection (PSI) is a cryptographic technique that allows two parties to compute
the intersection of their sets without revealing anything except the intersection. We use fully …

We present a new protocol for two-party private set intersection (PSI) with semi-honest
security in the plain model and one-sided malicious security in the random oracle model …

Private set intersection (PSI) allows two parties to compute the intersection of their sets
without revealing any information about items that are not in the intersection. It is one of the …

We present a new protocol for computing a circuit which implements the private set
intersection functionality (PSI). Using circuits for this task is advantageous over the usage of …

Large scale data processing brings new challenges to the design of privacy-preserving
protocols: how to meet the increasing requirements of speed and throughput of modern …

We present a new paradigm for multi-party private set intersection (PSI) that allows n parties
to compute the intersection of their datasets without revealing any additional information. We …

We introduce PyVertical, a framework supporting vertical federated learning using split
neural networks. The proposed framework allows a data scientist to train neural networks on …

In this work, we discuss our successful efforts for industry deployment of a cryptographic
secure computation protocol. The problem we consider is privately computing aggregate …