3 accepted papers
Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preser…
In privacy-preserving machine learning (PPML), homomorphic encryption (HE) has emerged as a significant primitive, allowing the use of machine learning (ML) models while protecting the confidentiality of input data. Although extensive research has been conducted on implementing PPML with HE by devel…
Embeddings, which compress information in raw text into semantics-preserving low-dimensional vectors, have been widely adopted for their efficacy. However, recent research has shown that embeddings can potentially leak private information about sensitive attributes of the text, and in some cases, ca…