论文标题
通过深度学习:设计和绩效评估,简短的区块长窃听通道代码
Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance Evaluation
论文作者
论文摘要
在信息理论安全保证的情况下,我们为高斯窃听通道设计了简短的区块长度代码。我们的方法在于将代码设计中的可靠性和保密性约束分解。具体来说,我们通过自动编码器处理可靠性约束,并处理具有哈希功能的保密约束。对于小于或等于128的区块长度,我们通过模拟合法接收器的错误概率以及我们的代码构建中的窃听器泄漏。该泄漏被定义为机密信息与窃听通道观察之间的共同信息,并通过基于神经网络的共同信息估计器进行经验测量。我们的仿真结果提供了具有正面保密率的代码的示例,这些代码的表现优于高斯窃听通道的非结构性可获得的最知名的保密率。此外,我们表明我们的代码设计适用于化合物和任意变化的高斯窃听通道,为此,通道统计信息不是完全清楚的,而是仅属于预先指定的不确定性集。这些模型不仅捕获了与渠道统计估计有关的不确定性,而且还捕获了窃听器堵塞合法传输或通过更改其位置来影响其自身渠道统计的场景。
We design short blocklength codes for the Gaussian wiretap channel under information-theoretic security guarantees. Our approach consists in decoupling the reliability and secrecy constraints in our code design. Specifically, we handle the reliability constraint via an autoencoder, and handle the secrecy constraint with hash functions. For blocklengths smaller than or equal to 128, we evaluate through simulations the probability of error at the legitimate receiver and the leakage at the eavesdropper for our code construction. This leakage is defined as the mutual information between the confidential message and the eavesdropper's channel observations, and is empirically measured via a neural network-based mutual information estimator. Our simulation results provide examples of codes with positive secrecy rates that outperform the best known achievable secrecy rates obtained non-constructively for the Gaussian wiretap channel. Additionally, we show that our code design is suitable for the compound and arbitrarily varying Gaussian wiretap channels, for which the channel statistics are not perfectly known but only known to belong to a pre-specified uncertainty set. These models not only capture uncertainty related to channel statistics estimation, but also scenarios where the eavesdropper jams the legitimate transmission or influences its own channel statistics by changing its location.