Sigma: Secure gpt inference with function secret sharing

K Gupta, N Jawalkar, A Mukherjee… - Cryptology ePrint …, 2023 - eprint.iacr.org
Abstract Secure 2-party computation (2PC) enables secure inference that offers protection
for both proprietary machine learning (ML) models and sensitive inputs to them. However …

A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems

HV Tran, T Chen, QVH Nguyen, Z Huang, L Cui… - arXiv preprint arXiv …, 2024 - arxiv.org
Since the creation of the Web, recommender systems (RSs) have been an indispensable
mechanism in information filtering. State-of-the-art RSs primarily depend on categorical …

Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference

K Maeng, GE Suh - arXiv preprint arXiv:2309.04875, 2023 - arxiv.org
Secure multi-party computation (MPC) allows users to offload machine learning inference on
untrusted servers without having to share their privacy-sensitive data. Despite their strong …

Private Information Retrieval with Access Control

P Goyal - 2023 - dspace.mit.edu
Private Information Retrieval (PIR) allows a user to query for a record from a remote
database without revealing the query to the database server. However, PIR does not provide …

Practical Privacy-preserving Machine Learning

Q Wang - 2024 - researchspace.auckland.ac.nz
Over the past decade, machine learning (ML) has experienced rapid progress, extending
from traditional image recognition tasks to advanced applications such as healthcare …

[引用][C] Decentralized on-device machine learning and unlearning for IoT collaboration

G Ye - 2023 - espace.library.uq.edu.au
The widespread use of mobile devices and Internet of Things (IoT) devices has resulted in
an enormous amount of data being generated by these devices. However, collecting and …