Computational wiretap coding: framework and practical construction

Abstract Wiretap coding, evolving in parallel with cryptography for nearly 50 years, focuses on secure transmission under the assumption that the wiretap channel is no less noisy than the main channel. Most provably secure schemes rely on information-theoretic security but often achieve limited practical rates. This paper proposes a framework for computationally secure modular wiretap coding. We integrate error correction encoding with the channel transition process to define the wiretap channel function, which consists of an invertible function and a lossy function. Secure encoding is then modeled as a computational entropy extractor. A detailed analysis of the lossy function for symmetric wiretap channels is presented. To leverage this lossiness, we design two computational extractors: the invertible fooling extractor (IFE) and the compressed randomness extractor (CRE). For practical implementation, we demonstrate that a 4-round optimal asymmetric encryption padding serves as an IFE in the random oracle model. Experimental comparisons show that our scheme achieves approximately 3 times and 2.7 times the code rates of the Invert-then-Encode and code-based schemes—classical information-theoretic schemes—under equivalent channel conditions. By instantiating IFE and CRE with hash algorithms such as SHAKE-128/256, we develop a practical wiretap coding scheme that achieves high rates with reasonable computational overhead.