In this paper we ad- dress the problem of securing sensitive NN inference pa- rameters against Power Analysis attacks. Our approach employs masking, a ...
Oct 22, 2024 · In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, ...
This paper proposes optimizations that exploit intrinsic characteristics of NN inference to reduce the masking's runtime and randomness requirements, ...
This paper exploits the implications of quantization on privacy leakage and proposes a novel quantization method that enhances the resistance of a neural ...
In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a ...
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A Masked Hardware Accelerator for Feed-Forward Neural Networks ...
www.researchgate.net › ... › Masks
In this article, we propose a masked hardware accelerator for feed-forward NNs that utilizes fixed-point arithmetic and is protected against side-channel ...
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Adam and Fei, Yunsi "Masking Feedforward Neural Networks Against Power Analysis Attacks" Proceedings on Privacy Enhancing Technologies , v.2022 , 2021 https ...
Apr 26, 2024 · We demonstrate GPAM effectiveness by carrying out power analysis attacks against four protected ECDSA implementations in Section 6. These ...
In this work, we propose the design of the first fully-masked neural network accelerator resistant against power-based side- channel attacks. We construct novel ...