The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …

Recently, private inference (PI) has addressed the rising concern over data and model
privacy in machine learning inference as a service. However, existing PI frameworks suffer …

Deep neural network (DNN) inference based on secure 2-party computation (2PC) can offer
cryptographically-secure privacy protection but suffers from orders of magnitude latency …

Transformer has been successfully used in practical applications, such as ChatGPT, due to
its powerful advantages. However, users' input is leaked to the model provider during the …

Prior work on Private Inference (PI)--inferences performed directly on encrypted input--has
focused on minimizing a network's ReLUs, which have been assumed to dominate PI …

The concern over data and model privacy in machine learning inference as a service
(MLaaS) has led to the development of private inference (PI) techniques. However, existing …

Designing privacy-preserving deep learning models is a major challenge within the deep
learning community. Homomorphic Encryption (HE) has emerged as one of the most …

With the growing use of large language models hosted on cloud platforms to offer inference
services, privacy concerns are escalating, especially concerning sensitive data like …

Stochastic computing (SC) has emerged as a promising computing paradigm for neural
acceleration. However, how to accelerate the state-of-the-art Vision Transformer (ViT) with …

With increasing concerns about privacy issues in deep learning, privacy-preserving neural
network inference has been receiving growing attention from the community, but the …