Paper 2023/1918

FANNG-MPC: Framework for Artificial Neural Networks and Generic MPC

Najwa Aaraj, Technology Innovation Institute
Abdelrahaman Aly, Technology Innovation Institute
Tim Güneysu, Ruhr-University Bochum
Chiara Marcolla, Technology Innovation Institute
Johannes Mono, Ruhr-University Bochum
Rogerio Paludo, Technology Innovation Institute
Iván Santos-González, Technology Innovation Institute
Mireia Scholz, Technology Innovation Institute
Eduardo Soria-Vazquez, Technology Innovation Institute
Victor Sucasas, Technology Innovation Institute
Ajith Suresh, Technology Innovation Institute

In this work, we introduce FANNG-MPC, a versatile secure multi-party computation framework capable to offer active security for privacy preserving machine learning as a service (MLaaS). Derived from the now deprecated SCALE-MAMBA, FANNG is a data-oriented fork, featuring novel set of libraries and instructions for realizing private neural networks, effectively reviving the popular framework. To the best of our knowledge, FANNG is the first MPC framework to offer actively secure MLaaS in the dishonest majority setting, specifically two parties. FANNG goes beyond SCALE-MAMBA by decoupling offline and online phases and materializing the dealer model in software, enabling a separate set of entities to produce offline material. The framework incorporates database support, a new instruction set for pre-processed material, including garbled circuits and convolutional and matrix multiplication triples. FANNG also implements novel private comparison protocols and an optimized library supporting Neural Network functionality. All our theoretical claims are substantiated by an extensive evaluation using an open-sourced implementation, including the private evaluation of popular neural networks like LeNet and VGG16.

Available format(s)
Cryptographic protocols
Publication info
Multi-Party ComputationPrivacy-Preserving Machine LearningHomomorphic EncryptionNeural NetworksMPCPPML
Contact author(s)
najwa aaraj @ tii ae
abdelrahaman aly @ gmail com
tim gueneysu @ rub de
chiara marcolla @ tii ae
johannes mono @ rub de
rogerio paludo @ tii ae
ivan santos @ tii ae
mireia scholz @ tii ae
eduardo soria-vazquez @ tii ae
victor sucasas @ tii ae
ajith suresh @ tii ae
2023-12-15: approved
2023-12-14: received
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      author = {Najwa Aaraj and Abdelrahaman Aly and Tim Güneysu and Chiara Marcolla and Johannes Mono and Rogerio Paludo and Iván Santos-González and Mireia Scholz and Eduardo Soria-Vazquez and Victor Sucasas and Ajith Suresh},
      title = {FANNG-MPC: Framework for Artificial Neural Networks and Generic MPC},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1918},
      year = {2023},
      note = {\url{}},
      url = {}
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