The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
머신러닝 보안 분야에서는 특히 엣지 디바이스에 대한 공격 표면 중 하나로 상관 전력/전자기 분석(CPA/CEMA) 등 사이드 채널 분석의 적용이 확대되고 있습니다. 신경망(NN) 모델 매개변수의 누출 저항을 평가하는 것을 목표로 합니다. 무게 및 편견, NN의 기본 연산인 부동소수점(FP) 연산에 대한 CPA/CEMA의 타당성 조사를 수행했습니다. 본 논문에서는 곱셈과 덧셈 연산에서 각각 CPA/CEMA를 사용하여 가중치와 편향을 복구하는 접근 방식을 제안합니다. 높은 정밀도와 효율성으로 복구를 실현하려면 IEEE 754 표현의 특성을 고려하는 것이 필수적입니다. FP 작업에 대한 CPA/CEMA에는 AES와 같은 암호화 구현에 대한 기존 CPA/CEMA와 다른 접근 방식이 필요하다는 것을 보여줍니다.
Hanae NOZAKI
National Institute of Advanced Industrial Science and Technology (AIST)
Kazukuni KOBARA
National Institute of Advanced Industrial Science and Technology (AIST)
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부
Hanae NOZAKI, Kazukuni KOBARA, "Power Analysis of Floating-Point Operations for Leakage Resistance Evaluation of Neural Network Model Parameters" in IEICE TRANSACTIONS on Fundamentals,
vol. E107-A, no. 3, pp. 331-343, March 2024, doi: 10.1587/transfun.2023CIP0012.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023CIP0012/_p
부
@ARTICLE{e107-a_3_331,
author={Hanae NOZAKI, Kazukuni KOBARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Power Analysis of Floating-Point Operations for Leakage Resistance Evaluation of Neural Network Model Parameters},
year={2024},
volume={E107-A},
number={3},
pages={331-343},
abstract={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.},
keywords={},
doi={10.1587/transfun.2023CIP0012},
ISSN={1745-1337},
month={March},}
부
TY - JOUR
TI - Power Analysis of Floating-Point Operations for Leakage Resistance Evaluation of Neural Network Model Parameters
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 331
EP - 343
AU - Hanae NOZAKI
AU - Kazukuni KOBARA
PY - 2024
DO - 10.1587/transfun.2023CIP0012
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E107-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2024
AB - 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.
ER -