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Title:
FANNG-MPC: Framework for Artificial Neural Networks and Generic MPC
Authors: Najwa Aaraj, Abdelrahaman Aly, Tim Güneysu, Chiara Marcolla, Johannes Mono, Rogerio Paludo, Iván Santos-González, Mireia Scholz, Eduardo Soria-Vazquez, Victor Sucasas, Ajith Suresh
Abstract: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.
ePrint: https://eprint.iacr.org/2023/1918
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