Low-Density Random Matrices for Secret Key Extraction

Secret key extraction, the task of extracting a secret key from shared information that is partially known by an eavesdropper, has important applications in cryptography. Motivated by the requirements of high-speed quantum key distribution, we study secret-key extraction methods with simple and efficient hardware implementations, in particular, linear transformations based on low-density random matrices. We show that this method can achieve the information-theoretic upper bound (conditional Shannon entropy) on efficiency for a wide range of key-distribution systems. In addition, we introduce a numerical method that allows us to tightly estimate the quality of the generated secret key in the regime of finite block length, and use this method to demonstrate that low-density random matrices achieve very high performance for secret key extraction.