Variational Probabilistic Quantization for Secret Key Generation

2026-09-02

AI Summary

本文提出了一种变分概率量化方法，通过神经编码器直接将相关源映射为离散密钥字母表，以解决在存在窃听者情况下的秘密密钥生成问题。

Abstract

Secret key generation from correlated observations at Alice and Bob, in the presence of an eavesdropper Eve, underpins physical-layer security. Classical pipelines quantize by hand, amplify privacy afterwards, and optimize no objective tied to a key rate. We propose Variational Probabilistic Quantization (VPQ): neural encoders that map the correlated sources directly into a discrete key alphabet, trained by a variational adversarial objective whose entropy, mismatch, and leakage terms match the three terms of the one-way secret key rate. A linear code-offset secure sketch then reconciles the encoder outputs into an identical key without a separate privacy amplification step. We prove that the VPQ losses lower-bound the one-way secret key capacity of the induced source, and derive in closed form the optimal worst-case key rate over the source class of a given alphabet size and mismatch probability, attained by finite-field linear sketches. On Gaussian fading channels, VPQ leaks less to a correlated eavesdropper than one classical and two recent learning-based baselines, and Reed--Solomon reconciliation operates within the predicted finite-blocklength gap.

Problem

• Secret key generation
• Correlated observations
• Eavesdropper
• Physical-layer security

Innovation

• Variational Probabilistic Quantization
• neural encoders
• variational adversarial objective
• linear code-offset secure sketch
• one-way secret key capacity