Many companies provide neural network prediction services to users for a wide range of
applications. However, current prediction systems compromise one party's privacy: either the …

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …

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 …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …

Machine learning models hosted in a cloud service are increasingly popular but risk privacy:
clients sending prediction requests to the service need to disclose potentially sensitive …

Secure computation enables mutually distrusting parties to jointly evaluate a function on
their private inputs without revealing anything but the function's output. Generic secure …

Abstract Machine learning classification is used in numerous settings nowadays, such as
medical or genomics predictions, spam detection, face recognition, and financial predictions …

Abstract Machine learning models trained on sensitive real-world data promise
improvements to everything from medical screening to disease outbreak discovery. In many …

Garbled circuits, a classical idea rooted in the work of Yao, have long been understood as a
cryptographic technique, not a cryptographic goal. Here we cull out a primitive …

For meeting diverse requirements of data analysis, the machine learning classifier has been
provided as a tool to evaluate data in many applications. Due to privacy concerns of …