ComputeFHE: A Privacy-Preserving General-Purpose Computation Library
arXiv SecurityArchived Jun 24, 2026✓ Full text saved
arXiv:2606.24379v1 Announce Type: new 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
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 23 Jun 2026]
ComputeFHE: A Privacy-Preserving General-Purpose Computation Library
Faris Serdar Tasel, Efe Ciftci
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.
Comments: 16 pages, 3 figures
Subjects: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
Cite as: arXiv:2606.24379 [cs.CR]
(or arXiv:2606.24379v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.24379
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Submission history
From: Faris Serdar Tasel [view email]
[v1] Tue, 23 Jun 2026 10:11:17 UTC (1,644 KB)
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