Large language models (LLMs) are complex artificial intelligence systems capable of
understanding, generating and translating human language. They learn language patterns …

We propose a new approach for privacy-preserving and verifiable convolutional neural
network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed …

Secure multi-party computation (MPC) enables collaboration on sensitive data while
maintaining privacy. In real-world scenarios, asymmetric trust assumptions are often most …

Secure multi-party computation (MPC) enables (joint) computations on sensitive data while
maintaining privacy. In real-world scenarios, asymmetric trust assumptions are often most …

Abstract Privacy-preserving Machine Learning as a Service (MLaaS) enables the resource-
limited client to cost-efficiently obtain inference output of a well-trained neural model that is …

Edge preprocessing is a critical service type in edge computing. However, untrusted edges
may be malicious to provide incorrect computational results (ie, edge tampering). Although …

In this paper, we present VerifyML, the first secure inference framework to check the fairness
degree of a given Machine learning (ML) model. VerifyML is generic and is immune to any …

Outsourcing inference enables users to outsource neural network inference tasks to a
service provider (eg, a remote server). This paradigm has brought enormous convenience …

The growing popularity of Machine Learning (ML) has led to its deployment in various
sensitive domains, which has resulted in significant research focused on ML security and …

The growing popularity of Machine Learning (ML) has led to its deployment in various
sensitive domains, which has resulted in significant research focused on ML security and …