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 …

Data privacy concerns are increasing significantly in the context of the Internet of Things,
cloud services, edge computing, artificial intelligence applications, and other applications …

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 …

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 …

Privacy-preserving machine learning algorithms are crucial for the increasingly common
setting in which personal data, such as medical or financial records, are analyzed. We …

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs)
use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal …

To improve the efficiency of big data feature learning, the paper proposes a privacy
preserving deep computation model by offloading the expensive operations to the cloud …