🤖 AI Summary
This work addresses the practical barriers to widespread adoption of fully homomorphic encryption (FHE)—notably its high computational overhead and programming complexity—by presenting an open-source C++ library built upon the TFHE scheme. The library enables developers to efficiently implement privacy-preserving algorithms using an imperative programming paradigm, supporting encrypted integer and fixed-point arithmetic, logical operations, comparisons, conditional execution, and oblivious array access. Its key innovation lies in a novel FHE-friendly optimized ALU architecture that substantially reduces the number of costly bootstrapping operations. Additionally, the framework incorporates a simulation mode for debugging and complexity analysis without requiring actual encryption or decryption. Experimental results demonstrate up to a 3.9× speedup on representative operations, significantly lowering bootstrapping overhead and providing a highly efficient and accessible foundation for FHE-based applications.
📝 Abstract
Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and development complexity. This paper presents ComputeFHE, an open-source C++ library that facilitates the development of privacy-preserving applications based on the TFHE cryptosystem. The library provides encrypted integer and fixed-point data types together with arithmetic, logical, comparison, conditional, and oblivious array-access operations which allow developers to implement algorithms using a familiar imperative programming paradigm. ComputeFHE supports both conventional TFHE arithmetic based on standard two-input logic gates and an optimized Arithmetic Logic Unit (ALU) architecture utilizing FHE-friendly logic primitives. Experimental results demonstrate significant reductions in the number of required bootstrapping operations, achieving performance improvements of up to 3.9x for selected operations. In addition, the library includes a simulation mode that enables testing, debugging, and complexity analysis without performing actual cryptographic computations while providing circuit complexity and bootstrapping costs. Built on top of OpenFHE, ComputeFHE offers a practical and accessible framework for developing and evaluating privacy-preserving algorithms and applications.