May 22, 2024, 4:11 a.m. | Hunjae "Timothy" Lee, Corey Clark

cs.CR updates on

arXiv:2310.13140v3 Announce Type: replace
Abstract: In the domain of Privacy-Preserving Machine Learning (PPML), Fully Homomorphic Encryption (FHE) is often used for encrypted computation to allow secure and privacy-preserving outsourcing of machine learning modeling. While FHE enables encrypted arithmetic operations, execution of programmatic logic such as control structures or conditional programming have remained a challenge. As a result, progress in encrypted training of PPML with FHE has been relatively stagnant compared to encrypted inference owing to the considerably higher logical complexity …

arxiv blind computation control domain encrypted encryption evaluation fhe framework fully homomorphic encryption homomorphic encryption logic machine machine learning modeling operations outsourcing privacy

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