Paper 2025/809

Don’t be mean: Reducing Approximation Noise in TFHE through Mean Compensation

Thomas de Ruijter, KU Leuven
Jan-Pieter D'Anvers, KU Leuven
Ingrid Verbauwhede, KU Leuven
Abstract

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without revealing any information about the data itself. However, FHE ciphertexts include noise for security reasons, which increases during operations and can lead to decryption errors. This paper addresses the noise introduced during bootstrapping in Torus Fully Homomorphic Encryption (TFHE), particularly focusing on approximation errors during modulus switching and gadget decomposition. We propose a mean compensation technique that removes the mean term from the noise equations, achieving up to a twofold reduction in noise variance. This method can be combined with bootstrap key unrolling for further noise reduction. Mean compensation can reduce the error probability of a standard parameter set from to , or allows the selection of more efficient parameters leading to a speedup of bootstrapping up to a factor .

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint.
Keywords
Fully homomorphic encryptionNoise analysisModulus switchingGadget decompositionBootstrap key unrolling
Contact author(s)
thomas deruijter @ esat kuleuven be
janpieter danvers @ esat kuleuven be
ingrid verbauwhede @ esat kuleuven be
History
2025-05-09: approved
2025-05-06: received
See all versions
Short URL
https://ia.cr/2025/809
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2025/809,
      author = {Thomas de Ruijter and Jan-Pieter D'Anvers and Ingrid Verbauwhede},
      title = {Don’t be mean: Reducing Approximation Noise in {TFHE} through Mean Compensation},
      howpublished = {Cryptology {ePrint} Archive, Paper 2025/809},
      year = {2025},
      url = {https://eprint.iacr.org/2025/809}
}
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