3 papers
cs.CR2026
AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
Zhaoting Gong, Ran Ran, Fan Yao +1
Fully Homomorphic Encryption (FHE) enables privacy-preserving Transformer inference, but long-sequence encrypted Transformers quickly exceed single-GPU memory capacity because enco…
cs.CR2026
Efficient Privacy-Preserving Sparse Matrix-Vector Multiplication Using Homomorphic Encryption
Yang Gao, Gang Quan, Wujie Wen +3
Sparse matrix-vector multiplication (SpMV) is a fundamental operation in scientific computing, data analysis, and machine learning. When the data being processed are sensitive, pre…
cs.CR2024
Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption
Yang Gao, Gang Quan, Soamar Homsi +2
Despite the cloud enormous technical and financial advantages, security and privacy have always been the primary concern for adopting cloud computing facility, especially for gover…