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Key Recovery Attacks on Approximate Homomorphic Encryption with Non-Worst-Case Noise Flooding Countermeasures

Guo, Qian LU ; Nabokov, Denis LU ; Suvanto, Elias LU and Johansson, Thomas LU orcid (2024) 33rd USENIX Security Symposium, USENIX Security 2024 p.7447-7461
Abstract

In this paper, we present novel key-recovery attacks on Approximate Homomorphic Encryption schemes, such as CKKS, when employing noise-flooding countermeasures based on non-worst-case noise estimation. Our attacks build upon and enhance the seminal work by Li and Micciancio at EUROCRYPT 2021. We demonstrate that relying on average-case noise estimation undermines noise-flooding countermeasures, even if the secure noise bounds derived from differential privacy as published by Li et al. at CRYPTO 2022 are implemented. This study emphasizes the necessity of adopting worst-case noise estimation in Approximate Homomorphic Encryption when sharing decryption results. We perform the proposed attacks on OpenFHE, an emerging open-source FHE... (More)

In this paper, we present novel key-recovery attacks on Approximate Homomorphic Encryption schemes, such as CKKS, when employing noise-flooding countermeasures based on non-worst-case noise estimation. Our attacks build upon and enhance the seminal work by Li and Micciancio at EUROCRYPT 2021. We demonstrate that relying on average-case noise estimation undermines noise-flooding countermeasures, even if the secure noise bounds derived from differential privacy as published by Li et al. at CRYPTO 2022 are implemented. This study emphasizes the necessity of adopting worst-case noise estimation in Approximate Homomorphic Encryption when sharing decryption results. We perform the proposed attacks on OpenFHE, an emerging open-source FHE library garnering increased attention. We experimentally demonstrate the ability to recover the secret key using just one shared decryption output. Furthermore, we investigate the implications of our findings for other libraries, such as IBM's HElib library, which allows experimental estimation of the noise bounds. Finally, we reveal that deterministic noise generation utilizing a pseudorandom generator fails to provide supplementary protection.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the 33rd USENIX Security Symposium
pages
15 pages
publisher
USENIX Association
conference name
33rd USENIX Security Symposium, USENIX Security 2024
conference location
Philadelphia, United States
conference dates
2024-08-14 - 2024-08-16
external identifiers
  • scopus:85202290857
ISBN
9781939133441
language
English
LU publication?
yes
id
4c3b7c52-ab26-4df9-9a7c-a27b2fc7163a
date added to LUP
2025-01-16 11:05:37
date last changed
2025-04-04 14:54:45
@inproceedings{4c3b7c52-ab26-4df9-9a7c-a27b2fc7163a,
  abstract     = {{<p>In this paper, we present novel key-recovery attacks on Approximate Homomorphic Encryption schemes, such as CKKS, when employing noise-flooding countermeasures based on non-worst-case noise estimation. Our attacks build upon and enhance the seminal work by Li and Micciancio at EUROCRYPT 2021. We demonstrate that relying on average-case noise estimation undermines noise-flooding countermeasures, even if the secure noise bounds derived from differential privacy as published by Li et al. at CRYPTO 2022 are implemented. This study emphasizes the necessity of adopting worst-case noise estimation in Approximate Homomorphic Encryption when sharing decryption results. We perform the proposed attacks on OpenFHE, an emerging open-source FHE library garnering increased attention. We experimentally demonstrate the ability to recover the secret key using just one shared decryption output. Furthermore, we investigate the implications of our findings for other libraries, such as IBM's HElib library, which allows experimental estimation of the noise bounds. Finally, we reveal that deterministic noise generation utilizing a pseudorandom generator fails to provide supplementary protection.</p>}},
  author       = {{Guo, Qian and Nabokov, Denis and Suvanto, Elias and Johansson, Thomas}},
  booktitle    = {{Proceedings of the 33rd USENIX Security Symposium}},
  isbn         = {{9781939133441}},
  language     = {{eng}},
  pages        = {{7447--7461}},
  publisher    = {{USENIX Association}},
  title        = {{Key Recovery Attacks on Approximate Homomorphic Encryption with Non-Worst-Case Noise Flooding Countermeasures}},
  year         = {{2024}},
}