The security of public-key cryptosystems relies on computationally hard problems, that are classically analyzed by number theoretic methods. In this paper, we introduce a new perspective on cryptosystems by interpreting the Diffie-Hellman key exchange as a nonlinear dynamical system. Employing Koopman theory, we transfer this dynamical system into a higher-dimensional space to analytically derive a purely linear system that equivalently describes the underlying cryptosystem. In this form, analytic tools for linear systems allow us to reconstruct the secret integers of the key exchange by simple manipulations. Moreover, we provide an upper bound on the minimal required lifting dimension to obtain perfect accuracy. To demonstrate the potential of our method, we relate our findings to existing results on algorithmic complexity. Finally, we transfer this approach to a data-driven setting where the Koopman representation is learned from data samples of the cryptosystem.
翻译:公用钥匙加密系统的安全依赖于计算上的难题, 通常用数字理论方法来分析这些难题。 在本文中, 我们通过将 Diffie- Hellman 键交换解释为非线性动态系统, 引入了对加密系统的新视角。 使用 Koopman 理论, 我们将这种动态系统转换为高维空间, 以分析方式得出一个纯粹的线性系统, 以等量描述隐性系统。 在这种形式中, 线性系统的分析工具允许我们通过简单的操作来重建关键交换的机密整数。 此外, 我们为获得完全准确性提供了最起码的提升维度的上限。 为了展示我们的方法的潜力, 我们将我们的调查结果与现有的算法复杂性结果联系起来。 最后, 我们将这一方法转移到一个数据驱动环境, 从加密系统的数据样本中学习 Koopman 代表 。