Power Analysis of Floating-Point Operations for Leakage Resistance Evaluation of Neural Network Model Parameters
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- NOZAKI Hanae
- National Institute of Advanced Industrial Science and Technology (AIST)
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- KOBARA Kazukuni
- National Institute of Advanced Industrial Science and Technology (AIST)
抄録
<p>In the field of machine learning security, as one of the attack surfaces especially for edge devices, the application of side-channel analysis such as correlation power/electromagnetic analysis (CPA/CEMA) is expanding. Aiming to evaluate the leakage resistance of neural network (NN) model parameters, i.e. weights and biases, we conducted a feasibility study of CPA/CEMA on floating-point (FP) operations, which are the basic operations of NNs. This paper proposes approaches to recover weights and biases using CPA/CEMA on multiplication and addition operations, respectively. It is essential to take into account the characteristics of the IEEE 754 representation in order to realize the recovery with high precision and efficiency. We show that CPA/CEMA on FP operations requires different approaches than traditional CPA/CEMA on cryptographic implementations such as the AES.</p>
収録刊行物
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- IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E107.A (3), 331-343, 2024-03-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390017843890667264
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- ISSN
- 17451337
- 09168508
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可