Sparse Regression Codes for Secret Key Agreement: Achieving Strong Secrecy and Near-Optimal Rates for Gaussian Sources
Emmanouil M. Athanasakos, Hariprasad Manjunath

TL;DR
This paper introduces a constructive protocol using Sparse Regression Codes for secret key agreement from Gaussian sources, achieving near-optimal rates with strong secrecy guarantees and analyzing the trade-offs involved.
Contribution
It provides the first comprehensive analysis of SPARCs for secret key agreement, demonstrating their near-optimal performance and practical advantages over existing methods.
Findings
Achieves near-optimal secret key rates with strong secrecy guarantees.
Characterizes the trade-off between key rate and public communication overhead.
Identifies a tunable parameter that maximizes secret key rate under practical constraints.
Abstract
Secret key agreement from correlated physical layer observations is a cornerstone of information-theoretic security. This paper proposes and rigorously analyzes a complete, constructive protocol for secret key agreement from Gaussian sources using Sparse Regression Codes (SPARCs). Our protocol systematically leverages the known optimality of SPARCs for both rate-distortion and Wyner-Ziv (WZ) coding, facilitated by their inherent nested structure. The primary contribution of this work is a comprehensive end-to-end analysis demonstrating that the proposed scheme achieves near-optimal secret key rates with strong secrecy guarantees, as quantified by a vanishing variational distance. We explicitly characterize the gap to the optimal rate, revealing a fundamental trade-off between the key rate and the required public communication overhead, which is governed by a tunable quantization…
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