Private AI: Machine Learning on Encrypted Data

DOI10.1007/978-3-030-86236-7_6OpenAlexW3159023066MaRDI QIDQ6191257

Kristin E. Lauter

Publication date: 7 March 2024

Published in: SEMA SIMAI Springer Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-86236-7_6

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Data encryption (aspects in computer science) (68P25) Privacy of data (68P27)

Cites Work

• Unnamed Item
• On the concrete hardness of learning with errors
• Factoring polynomials with rational coefficients
• CHIMERA: combining ring-LWE-based fully homomorphic encryption schemes
• TFHE: fast fully homomorphic encryption over the torus
• Homomorphic encryption for arithmetic of approximate numbers
• (Leveled) fully homomorphic encryption without bootstrapping
• Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme
• Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP
• Private Computation on Encrypted Genomic Data
• FHEW: Bootstrapping Homomorphic Encryption in Less Than a Second
• BKZ 2.0: Better Lattice Security Estimates
• A method for obtaining digital signatures and public-key cryptosystems
• ML Confidential: Machine Learning on Encrypted Data
• Homomorphic Encryption Standard
• Fully homomorphic encryption using ideal lattices
• On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption
• Efficient Fully Homomorphic Encryption from (Standard) LWE
• Theory of Cryptography

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