Paper 2025/2027

Accurate BGV Parameters Selection: Accounting for Secret and Public Key Dependencies in Average-Case Analysis

Chiara Marcolla, Technology Innovation Institute

Nadir Murru, University of Trento

Matilda Urani, Polytechnic University of Turin

Abstract

The Brakerski-Gentry-Vaikuntanathan (BGV) scheme is one of the most significant fully homomorphic encryption (FHE) schemes. It belongs to a class of FHE schemes whose security is based on the presumed intractability of the Learning with Errors (LWE) problem and its ring variant (RLWE). Such schemes deal with a quantity, called noise, which increases each time a homomorphic operation is performed. Specifically, in order for the scheme to work properly, it is essential that the noise remains below a certain threshold throughout the process. For BGV, this threshold strictly depends on the ciphertext modulus, which is one of the initial parameters whose selection heavily affects both the efficiency and security of the scheme. For an optimal parameter choice, it is crucial to accurately estimate the noise growth, particularly that arising from multiplication, which is the most complex operation. In this work, we propose a novel average-case approach that precisely models noise evolution and guides the selection of initial parameters, improving efficiency while ensuring security. The key innovation of our method lies in accounting for the dependencies among ciphertext errors generated with the same key, and in providing general guidelines for accurate parameter selection that are library-independent. Our parameter selection methodology leads to a significant improvement over the parameter choices currently adopted in major FHE libraries.

@misc{cryptoeprint:2025/2027,
      author = {Beatrice Biasioli and Chiara Marcolla and Nadir Murru and Matilda Urani},
      title = {Accurate {BGV} Parameters Selection: Accounting for Secret and Public Key Dependencies in Average-Case Analysis},
      howpublished = {Cryptology {ePrint} Archive, Paper 2025/2027},
      year = {2025},
      url = {https://eprint.iacr.org/2025/2027}
}