ML Confidential: Machine Learning on Encrypted Data
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Publication:4922858
DOI10.1007/978-3-642-37682-5_1zbMath1293.68110OpenAlexW133884053MaRDI QIDQ4922858
Thore Graepel, Michael Naehrig, Kristin E. Lauter
Publication date: 4 June 2013
Published in: Lecture Notes in Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-642-37682-5_1
Learning and adaptive systems in artificial intelligence (68T05) Cryptography (94A60) Data encryption (aspects in computer science) (68P25)
Related Items (24)
Provably Weak Instances of Ring-LWE ⋮ Homomorphic lower digits removal and improved FHE bootstrapping ⋮ Privacy-preserving computation in cyber-physical-social systems: a survey of the state-of-the-art and perspectives ⋮ Privacy preservation for machine learning training and classification based on homomorphic encryption schemes ⋮ Stream ciphers: a practical solution for efficient homomorphic-ciphertext compression ⋮ Depth Optimized Efficient Homomorphic Sorting ⋮ Private Computation on Encrypted Genomic Data ⋮ Efficient Integer Encoding for Homomorphic Encryption via Ring Isomorphisms ⋮ Efficient Evaluation of Low Degree Multivariate Polynomials in Ring-LWE Homomorphic Encryption Schemes ⋮ Differentially private naive Bayes learning over multiple data sources ⋮ Homomorphic secret sharing for multipartite and general adversary structures supporting parallel evaluation of low-degree polynomials ⋮ Privacy preserving multi-party computation delegation for deep learning in cloud computing ⋮ Private AI: Machine Learning on Encrypted Data ⋮ Fully Homomorphic Encryption for Point Numbers ⋮ A full RNS variant of approximate homomorphic encryption ⋮ Unsupervised Machine Learning on encrypted data ⋮ Stream Ciphers: A Practical Solution for Efficient Homomorphic-Ciphertext Compression ⋮ Somewhat/Fully Homomorphic Encryption: Implementation Progresses and Challenges ⋮ Unnamed Item ⋮ Order-Revealing Encryption and the Hardness of Private Learning ⋮ Encoding of Rational Numbers and Their Homomorphic Computations for FHE-Based Applications ⋮ SPEED: secure, private, and efficient deep learning ⋮ Accelerating Homomorphic Computations on Rational Numbers ⋮ A geometric approach to homomorphic secret sharing
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