Maliciously secure matrix multiplication with applications to private deep learning
From MaRDI portal
Publication:2691579
DOI10.1007/978-3-030-64840-4_2OpenAlexW3022452713MaRDI QIDQ2691579
Hao Chen, Ilya Razenshteyn, Yongsoo Song, Miran Kim, Sameer Wagh, Dragos Rotaru
Publication date: 29 March 2023
Full work available at URL: https://doi.org/10.1007/978-3-030-64840-4_2
Related Items (8)
Improving the efficiency of AES protocols in multi-party computation ⋮ \( \mathsf{Rabbit} \): efficient comparison for secure multi-party computation ⋮ Improved threshold signatures, proactive secret sharing, and input certification from LSS isomorphisms ⋮ High-precision bootstrapping for approximate homomorphic encryption by error variance minimization ⋮ Putting the online phase on a diet: covert security from short MACs ⋮ Finding and evaluating parameters for BGV ⋮ Toward practical lattice-based proof of knowledge from Hint-MLWE ⋮ On the Scaled Inverse of $(x^i-x^j)$ modulo Cyclotomic Polynomial of the form $\Phi_{p^s}(x)$ or $\Phi_{p^s q^t}(x)$
Uses Software
Cites Work
- (Leveled) Fully Homomorphic Encryption without Bootstrapping
- Overdrive: making SPDZ great again
- Faster homomorphic linear transformations in HElib
- Security and composition of multiparty cryptographic protocols
- EPIC: efficient private image classification (or: learning from the masters)
- Homomorphic encryption for arithmetic of approximate numbers
- Algorithms in HElib
- Multiparty Computation from Somewhat Homomorphic Encryption
- Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP
- Practical Covertly Secure MPC for Dishonest Majority – Or: Breaking the SPDZ Limits
- Better Zero-Knowledge Proofs for Lattice Encryption and Their Application to Group Signatures
- Semi-homomorphic Encryption and Multiparty Computation
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