The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the
client and the server and is a promising technique in the machine-learning-as-a-service …

Federated learning enables clients to train a machine learning model jointly without sharing
their local data. However, due to the centrality of federated learning framework and the …

Neural networks (NNs) have become one of the most important tools for artificial
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …

We initiate the study of private inference on Transformer-based models in the client-server
setting, where clients have private inputs and servers hold proprietary models. Our main …

Federated learning (FL) is an increasingly popular approach for machine learning (ML) in
cases where the training dataset is highly distributed. Clients perform local training on their …

Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …

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

Inference of machine learning models with data privacy guarantees has been widely studied
as privacy concerns are getting growing attention from the community. Among others, secure …

We studied 53 privacy-preserving neural-network papers in 2016-2022 based on
cryptography (without trusted processors or differential privacy), 16 of which only use …