Paper 2020/1225

ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame


Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly evaluate a function on their private inputs while maintaining input privacy. In this work, we improve semi-honest secure two-party computation (2PC) over rings, with a focus on the efficiency of the online phase. We propose an efficient mixed-protocol framework, outperforming the state-of-the-art 2PC framework of ABY. Moreover, we extend our techniques to multi- input multiplication gates without inflating the online communication, i.e., it remains independent of the fan-in. Along the way, we construct efficient protocols for several primitives such as scalar product, matrix multiplication, comparison, maxpool, and equality testing. The online communication of our scalar product is two ring elements irrespective of the vector dimension, which is a feature achieved for the first time in the 2PC literature. The practicality of our new set of protocols is showcased with four applications: i) AES S-box, ii) Circuit-based Private Set Intersection, iii) Biometric Matching, and iv) Privacy- preserving Machine Learning (PPML). Most notably, for PPML, we implement and benchmark training and inference of Logistic Regression and Neural Networks over LAN and WAN networks. For training, we improve online runtime (both for LAN and WAN) over SecureML (Mohassel et al., IEEE S&P’17) in the range 1.5x-6.1x, while for inference, the improvements are in the range of 2.5x-754.3x.

Note: This article is the full and extended version of an article published at USENIX Security’21. Added the details of Braun et al., PriML@NeurIPS'21, which contains implementations of our ABY2.0 protocols along with further optimizations and more protocols.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. Major revision. 30th USENIX Security Symposium (USENIX Security '21)
multi-party computation2PCABYprivacy-preserving machine learningPPML
Contact author(s)
arpita @ iisc ac in
schneider @ encrypto cs tu-darmstadt de
suresh @ encrypto cs tu-darmstadt de
ajith @ iisc ac in
yalame @ encrypto cs tu-darmstadt de
2022-01-26: last of 4 revisions
2020-10-06: received
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Creative Commons Attribution


      author = {Arpita Patra and Thomas Schneider and Ajith Suresh and Hossein Yalame},
      title = {{ABY2}.0: Improved Mixed-Protocol Secure Two-Party Computation},
      howpublished = {Cryptology ePrint Archive, Paper 2020/1225},
      year = {2020},
      note = {\url{}},
      url = {}
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