International Association
for Cryptologic Research

In recent years, the research community has made great progress in improving techniques for privacy-preserving computation, such as fully homomorphic encryption (FHE). Despite the progress, there remain open challenges, mainly in performance and usability, to further advance the adoption of these technologies. This work provides multiple contributions that improve the current state-of-the-art in both areas. More specifically, we significantly simplify the multi-value bootstrapping by Carpov, Izabachène, and Mollimard [CIM19] for Boolean-based FHE schemes such as FHEW or TFHE, making the concept usable in practice. Based on our simplifications, we implement an easy-to-use interface for multi-value bootstrapping in the open-source library FHE-Deck [fhe23], derive new parameter sets for multi-bit encryptions with state-of-the-art security, and build a toolset that translates high-level code to multi-bit operations on encrypted data using circuit synthesis. We propose and integrate the first non-trivial FHE-specific optimizations for privacy-preserving circuit synthesis: look-up table (LUT) grouping and adder substitution. Using LUT grouping, we reduce the number of bootstrapping operations by almost 40% on average, while for adder substitution, we reduce the number of required bootstrappings by up to 85% for certain use cases. Overall, the execution time is up to 4.2x faster with all optimizations enabled compared to previous state-of-the-art circuit synthesis.

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.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 inference of popular neural networks like LeNet and VGG16.