Exposing Side-Channel Leakage of SEAL Homomorphic Encryption Library
Exposing Side-Channel Leakage of SEAL Homomorphic Encryption Library
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DOI：
10.1145/3560834.3563833
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Furkan Aydin;Aydin Aysu
中科院分区：
文献类型：
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作者：
Furkan Aydin;Aydin Aysu
本文揭示了一个新的侧通道泄漏的Microsoft SEAL同态加密库。所提出的攻击利用泄漏的三进制值分配期间的数论变换（NTT）子例程。值得注意的是，攻击可以从单个功率/电磁测量迹线窃取秘密密钥系数。为了实现高精度的单道，我们建立了一个新的基于机器学习的侧通道轮廓。此外，我们实现了基于随机延迟插入的防御机制，以减轻所示的泄漏。在ARM Cortex-M4 F处理器上的实验结果表明，该攻击提取密钥系数的准确率为98.3%，随机延迟插入防御并没有降低攻击的成功率。
This paper reveals a new side-channel leakage of Microsoft SEAL homomorphic encryption library. The proposed attack exploits the leakage of ternary value assignments made during the Number Theoretic Transform (NTT) sub-routine. Notably, the attack can steal the secret key coefficients from a single power/electromagnetic measurement trace. To achieve high accuracy with a single-trace, we build a novel machine-learning based side-channel profiler. Moreover, we implement a defense based on random delay insertion based defense mechanism to mitigate the shown leakage. The results on an ARM Cortex-M4F processor show that our attack extracts secret key coefficients with 98.3% accuracy and random delay insertion defense does not reduce the success rate of our attack.