We propose a lattice-based scheme for secret key generation from Gaussian sources in the presence of an eavesdropper, and show that it achieves the strong secret key capacity in the case of degraded source models, as well as the optimal secret key / public communication rate trade-off. The key ingredients of our scheme are the use of the modulo lattice operation to extract the channel intrinsic randomness, based on the notion of flatness factor, together with a randomized lattice quantization technique to quantize the continuous source. Compared to previous works, we introduce two new notions of flatness factor based on $L^1$ distance and KL divergence, respectively, which might be of independent interest. We prove the existence of secrecy-good lattices under $L^1$ distance and KL divergence, whose $L^1$ and KL flatness factors vanish for volume-to-noise ratios up to $2\pi e$. This improves upon the volume-to-noise ratio threshold $2\pi$ of the $L^{\infty}$ flatness factor.
翻译:我们提出了一种基于格的方案,用于在窃听器存在的情况下从高斯源中生成秘密密钥,并证明了该方案在降级的源模型中实现了强秘钥容量,并获得了最佳秘密密钥/公共通信速率的权衡。我们方案的关键因素是使用模格操作来提取通道内在的随机性,基于平直度因子的概念,以及随机化格量化技术来量化连续源。与以往的研究相比,我们引入了两个基于$L^1$距离和KL散度的平直度因子的新概念,这可能是具有独立兴趣的两个概念。我们证明了$L^1$距离和KL散度下保密良好格的存在性,其$L^1$和KL平直度因子在体积信噪比高达$2\pi e$的情况下消失。这优于$L^{\infty}$平直度因子的体积信噪比阈值$2\pi$。