ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation

By Daniel Demmler, Thomas Schneider, Michael Zohner. 2015

928 citations recorded by the source corpus.

from details of underlying secure computation protocol Use only fast symmetric key crypto Code is available on GitHub: http://encrypto.de/code/ABY

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Privacy & Security

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Citations in the corpus

QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019, methodology): “For (ii) we use a vectorized version of the OT-based multiplication protocol presented originally in [16], and used in [11, 41].”
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Let us thus first argue security of our protocols for the inner product functionality: while for the security of the OTbased protocol we refer the reader to [18, 29], we state the correctness and security of the protocol based on a Trusted Initializer in the following lemma.”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “For converting between the different sharing types, we use the methods from the ABY framework [34] which are based on highly efficient OT extensions.”
Blind Justice: Fairness with Encrypted Sensitive Attributes (2018, methodology): “Several generic protocols for MPC exists, such as the ones based on arithmetic sharing (Damg˚ard et al., 2012), garbled circuits (Yao, 1986), or GMW (Goldreich et al., 1987), with several available implementations (Demmler et al., 2015b; Zahur & Evans, 2015b).”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “To facilitate secure computations, C RYP T EN implements arithmetic secret sharing [23, 24] and binary secret sharing [33], as well as conversions between these two types of sharing [25].”
SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017, methodology): “We briefly review these schemes but refer the reader to [18] for more details.”
Secure Outsourced Matrix Computation and Application to Neural Networks (2018, neutral): “Mohassel and Zhang [39] applied the mixed-protocol framework of [17] to implement neural networks training and evaluation in a two-party computation setting.”
ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, methodology): “In this work, we focus on the specific problem of secure two-party computation (2PC) [40, 41] with mixed protocols over rings.”
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, methodology): “EzPC [20] is a ABY-based [39] secure computation framework that translates high-level programs into Boolean and arithmetic circuits.”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, background): “This framework only implements two-party computation with semi-honest security [32].”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, methodology): “It is straightforward to replace LHE with the optimized HE scheme in [8] or garbled circuits with the mixed protocol ABY framework [10].”