Data Scientist Ke Zhang’s Research Explores Homomorphic Encryption for Privacy-Preserving Marketing

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted consumer data. By combining ...
IEEE

An Efficient and Privacy-Preserving Federated Learning Approach Based on Homomorphic Encryption

Abstract: Federated Learning (FL) is a decentralized and collaborative learning approach that ensures the data privacy of each participant. However, recent studies have shown that the private data of ...
IEEE

LEAM: A Low-Area and Efficient Accelerator of Matrix-Vector Multiplication for Homomorphic Encryption

Abstract: Homomorphic encryption (HE) is regarded as one of the most promising techniques for privacy-preserving computing. However, the enormous data and computations form the bottleneck of its ...