Title: Make Shuffling Great Again: A Side-Channel Resistant Fisher-Yates Algorithm for Protecting Neural Networks Show Chinese title

Title: 让洗牌再次伟大：一种侧信道抗性Fisher-Yates算法，用于保护神经网络

Abstract: Neural network models implemented in embedded devices have been shown to be susceptible to side-channel attacks (SCAs), allowing recovery of proprietary model parameters, such as weights and biases. There are already available countermeasure methods currently used for protecting cryptographic implementations that can be tailored to protect embedded neural network models. Shuffling, a hiding-based countermeasure that randomly shuffles the order of computations, was shown to be vulnerable to SCA when the Fisher-Yates algorithm is used. In this paper, we propose a design of an SCA-secure version of the Fisher-Yates algorithm. By integrating the masking technique for modular reduction and Blakely's method for modular multiplication, we effectively remove the vulnerability in the division operation that led to side-channel leakage in the original version of the algorithm. We experimentally evaluate that the countermeasure is effective against SCA by implementing a correlation power analysis attack on an embedded neural network model implemented on ARM Cortex-M4. Compared to the original proposal, the memory overhead is $2\times$ the biggest layer of the network, while the time overhead varies from $4\%$ to $0.49\%$ for a layer with $100$ and $1000$ neurons, respectively. Show Chinese abstract

Abstract: 在嵌入式设备中实现的神经网络模型已被证明容易受到侧信道攻击（SCAs），这使得可以恢复专有的模型参数，如权重和偏差。 目前已有可用于保护密码实现的对抗措施方法，这些方法可以调整以保护嵌入式神经网络模型。 混洗是一种基于隐藏的对抗措施，它随机打乱计算顺序，当使用Fisher-Yates算法时，已被证明对SCA存在漏洞。 在本文中，我们提出了一种Fisher-Yates算法的SCA安全版本的设计。 通过将掩码技术用于模运算和Blakely的模乘法方法相结合，我们有效地消除了导致原始算法中侧信道泄露的除法操作中的漏洞。 我们通过在基于ARM Cortex-M4的嵌入式神经网络模型上实施相关功耗分析攻击，实验评估了该对抗措施对SCA的有效性。 与原始方案相比，内存开销为网络最大层的$2\times$，而时间开销则根据具有$100$和$1000$个神经元的层分别从$4\%$变化到$0.49\%$。