Bake It Till You Make It: Heat-induced Leakage from Masked Neural Networks
Bake It Till You Make It: Heat-induced Leakage from Masked Neural Networks
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发表时间：
2023
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通讯作者：
Devyani M. Mehta;M. Hashemi;D. Koblah;Domenic Forte;F. Ganji
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作者：
Devyani M. Mehta;M. Hashemi;D. Koblah;Domenic Forte;F. Ganji
.屏蔽已成为保护硬件设计免受侧信道攻击的最有效方法之一。无论如何努力在现场可编程门阵列（FPGA）上正确地实现掩蔽方案，都可能意外地观察到泄漏。这是由于所有掩蔽设计的基础假设，即，不同份额的漏损是相互独立的，在实践中可能不再成立。在这方面，极端温度已被证明是诱发泄漏的重要因素，即使在正确掩蔽的设计中。这先前已经使用外部热发生器（即，气候室）。在本文中，我们研究是否可以使用电路元件本身引起的泄漏。具体来说，我们的目标是FPGA中的掩码神经网络（NN），其中一个主要构建模块是块随机存取存储器（BRAM）和触发器（FF）。在这方面，由于NN的固有特性，我们的新型内部热发生器仅利用专门用于存储用户输入的存储器，特别是当频繁地将交替模式写入BRAM和FF时。观察一阶泄漏的可能性进行评估，考虑最近和成功的一阶安全掩蔽NN之一，即ModuloNET。ModuloNET是专门为FPGA设计的，其中BRAM用于存储输入和中间计算。我们的实验结果表明，不希望的一阶泄漏可以观察到通过增加温度时，交替输入施加到掩蔽NN。为了更好地理解极端高温的影响，我们进一步对FF存储输入的设计进行了类似的测试，其中可以得出相同的结论。
. Masking has become one of the most effective approaches for securing hardware designs against side-channel attacks. Irrespective of the effort put into correctly implementing masking schemes on a field programmable gate array (FPGA), leakage can be unexpectedly observed. This is due to the fact that the assumption underlying all masked designs, i.e., the leakages of different shares are independent of each other, may no longer hold in practice. In this regard, extreme temperatures have been shown to be an important factor in inducing leakage, even in correctly-masked designs. This has previously been verified using an external heat generator (i.e., a climate chamber). In this paper, we examine whether the leakage can be induced using the circuit components themselves. Specifically, we target masked neural networks (NNs) in FPGAs, with one of the main building blocks being block random access memory (BRAM) and flip-flops (FFs). In this respect, thanks to the inherent characteristics of NNs, our novel internal heat generators leverage solely the memories devoted to storing the user’s input, especially when frequently writing alternating patterns into BRAMs and FFs. The possibility of observing first-order leakage is evaluated by considering one of the most recent and successful first-order secure masked NNs, namely ModuloNET. ModuloNET is specifically designed for FPGAs, where BRAMs are used for storing the inputs and intermediate computations. Our experimental results demonstrate that undesirable first-order leakage can be observed by increasing the temperature when an alternating input is applied to the masked NN. To give a better understanding of the impact of extreme heat, we further perform a similar test on the design with FFs storing the input, where the same conclusion can be drawn.