We study masked implementations' security when the adversary can randomly probe their internal variables. By describing the relations of the intermediate variables with a parity equation system, we assess the random probing leakage's informativeness with a new definition for the security. Side-channel researchers often consider the Bayesian adversary, here we introduce the MAP adversary and discuss that she has the highest possible success rate among the other adversaries. For various masked implementations, the security as a function of masking order and leakage rate is measured. In contrast to the previous results in the asymptomatic model, our approach is in a concrete setting. Therefore, it can be used as an analysis tool for practical engineering purposes. Moreover, for the multiplication gadget proposed in Ches 2016, with some modification, we prove security in the random probing for constant leakage rate. So, we give the first practical multiplication gadget with proved security in the random probing model. As another contribution, leakage effects of refreshing gadgets is modeled with an equivalent erasure channel. Appropriate handling of the leakage of refreshing gadgets, instead of neglecting, was a long-standing challenge in the random probing environment. This modeling helps to give the first S-Box implementation with proved security in the random probing leakage. We also study the security of arbitrary order masking of AES, and for the first time, we derive a security bound that is independent of the size of masked implementation. Furthermore, we have developed new insights into the connections of the SNI security in the threshold probing model with the security results obtained in the random probing model.
翻译:当对手可以随机检测其内部变量时,我们研究掩蔽执行的安全性。 通过描述中间变量与对等方方程系统的关系, 我们评估随机检测渗漏的知情性, 并给出新的安全定义。 侧通道研究人员通常会考虑巴伊西亚对手, 我们在这里介绍MAP对手, 并讨论她在其他对手中具有尽可能高的成功率。 对于各种掩蔽执行, 安全作为掩蔽秩序和渗漏率的函数被测量。 与以往的无症状模型模型相比, 我们的方法是在一个混凝土设置中。 因此, 我们可以用它来作为一个分析工具, 用于实际的工程目的。 此外, 对于2016年在切斯提出的递增工具, 侧道研究人员往往会考虑巴伊亚的对手, 我们用随机的测试来证明她的安全性。 因此, 我们给第一个实际的倍增倍增, 随机检测模型显示她的安全性。