Exposing Side-Channel Leakage of SEAL Homomorphic Encryption Library

Aydin, Furkan; Aysu, Aydın

doi:10.1145/3560834.3563833

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Since there is a certain clock cycle to align with, it is not enough to just translate the acquisitions. Such deformations can be managed with the use of certain realignment methods, such as [15]. Here, we want to demonstrate that the realignment pre-processing can be eliminated, allowing the CNN deep structure to implicitly handle it.…”

Section: Resultsmentioning

confidence: 99%

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Abbas Ahmed,

Kamrul Hasan,

Azman Mohd Noah

et al. 2024

IJCDS

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This work explores a novel method of SCA profiling to address compatibility problems and strengthen Deep Learning (DL) models. Convolutional Neural Networks are proposed in this research as a countermeasure to misalignment-focused countermeasures. "Time-Delay Convolutional Neural Networks" (TDCNN) is more accurate than "Convolutional Neural Network," yet it's still acceptable. It's true that TDCNNs are neural networks based on convolution learned on single spatial information, just as side-channel tracings. However, given to recent surge in popularity of CNNs, particularly from the year 2012 when CNN framework ("AlexNet") achieved Image Net Large Scale Visual Recognition Competition which is a notable image detection competition, a novel TDCNN has been termed out in DL literature. Currently, it needs to employ the characteristics related to CNN design, including declaring that one input feature equals 1 for instance, to establish a TDCNN in the most widely used DL libraries.

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Section: Resultsmentioning

confidence: 99%

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Abbas Ahmed,

Kamrul Hasan,

Azman Mohd Noah

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Compiler optimization (-O3 flag) improves the performance of the code. It performs several optimizations such as common subexpression elimination, loop invariant motion, constant folding, tailcall optimization and tail recursion, conditional execution or branch elimination, function inlining, loop restructuring, instruction scheduling, etc 1 . Fig.…”

Section: Why Previous Side-channel Leakages Do Not Existmentioning

confidence: 99%

“…An earlier version of this article has been published in the proceedings of the 2022 Workshop on Attacks and Solutions in Hardware Security [1]. The major contributions of the conference edition were: This journal article enhances our prior work in several aspects as outlined below:…”

Section: Introductionmentioning

confidence: 95%

Leaking secrets in homomorphic encryption with side-channel attacks

Aydin,

Aysu

2024

J Cryptogr Eng

Self Cite

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Homomorphic encryption (HE) allows computing encrypted data in the ciphertext domain without knowing the encryption key. It is possible, however, to break fully homomorphic encryption (FHE) algorithms by using side channels. This article demonstrates side-channel leakages of the Microsoft SEAL HE library. The proposed attack can steal encryption keys during the key generation phase by abusing the leakage of ternary value assignments that occurs during the number theoretic transform (NTT) algorithm. We propose two attacks, one for -O0 flag non-optimized code implementation which targets addition and subtraction operations, and one for -O3 flag compiler optimization which targets guard and mul root operations. In particular, the attacks can steal the secret key coefficients from a single power/electromagnetic measurement trace of SEAL's NTT implementation. To achieve high accuracy with a single-trace, we develop novel machine-learning side-channel profilers. On an ARM Cortex-M4F processor, our attacks are able to extract secret key coefficients with an accuracy of 98.3% when compiler optimization is disabled, and 98.6% when compiler optimization is enabled. We finally demonstrate that our attack can evade an application of the random delay insertion defense.

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Leaking Secrets in Homomorphic Encryption with Side-Channel Attacks

Aydin

Aysu

2023

Preprint

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Homomorphic encryption (HE) allows computing encrypted data in the ciphertext domain without knowing the encryption key. It is possible, however, to break fully homomorphic encryption (FHE) algorithms by using side channels. This article demonstrates side-channel leakages of the Microsoft SEAL HE library. The proposed attack can steal encryption keys during the key generation phase by abusing the leakage of ternary value assignments that occurs during the number theoretic transform (NTT) algorithm. We propose two attacks, one for -O0 flag non-optimized code implementation which targets addition and subtraction operations, and one for -O3 flag compiler optimization which targets guard and mul root operations. In particular, the attacks can steal the secret key coefficients from a single power/electromagnetic measurement trace of SEAL’s NTT implementation. To achieve high accuracy with a single-trace, we develop novel machine-learning side-channel profilers. On an ARM Cortex-M4F processor, our attacks are able to extract secret key coefficients with an accuracy of 98.3% when compiler optimization is disabled, and 98.6% when compiler optimization is enabled. We finally demonstrate that our attack can evade an application of the random delay insertion defense.

show abstract

Exposing Side-Channel Leakage of SEAL Homomorphic Encryption Library

Cited by 3 publications

References 11 publications

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Leaking secrets in homomorphic encryption with side-channel attacks

Leaking Secrets in Homomorphic Encryption with Side-Channel Attacks

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