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Lecture notes in computer science

Min-Entropy Estimation for Physical Layer Key Generation: An Empirical Study

Dongchi Han, Yuan Ma, Tianyu Chen, Liliang Guan, Xianhui Lu

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-981-95-6209-1_22

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In physical-layer key generation (PLKG), researchers often assess key security using randomness tests or secret key capacity analysis. However, randomness tests can be misleading, as low-entropy inputs may still pass when obscured by hash functions or extractors. While key capacity captures the theoretical upper limit of secret bits from raw channels, it overlooks the impact of practical preprocessing. Recent studies have explored min-entropy as a more practical security metric. However, commonly used min-entropy estimators, such as those in NIST SP 800-90B (90B), may yield inaccurate results for complex sources, even overestimating entropy and thereby undermining security. Their applicability to PLKG also remains unverified. To address the above research gap, this paper focuses on modeling the received signal envelope, a common feature extracted for key generation. Leveraging typical wireless fading scenarios considered in key capacity analysis, we adopt the more general Weibull distribution to characterize the envelope statistics, which generalizes the Rayleigh model and better captures diverse propagation conditions. We first derive an explicit expression for the min-entropy in the absence of preprocessing, and then systematically analyze the impact of typical preprocessing operations on the statistical properties of the observed channel. Building on this foundation, we further establish theoretical upper and lower bounds on the min-entropy under representative preprocessing strategies. We further evaluate all 90B entropy estimators using both simulations and real-world channel measurements, comparing their estimates with our analytical results. While some estimators work well on raw data, they often fail or overestimate entropy after preprocessing, compromising security. In contrast, our conservative lower bound remains robust across all tested scenarios and preprocessing conditions. It offers a reliable fallback when standard estimators break down, providing a stronger theoretical foundation for secure key extraction in PLKG systems.

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Lecture notes in electrical engineering•Nov 23, 2016•0 citations
In physical-layer key generation (PLKG), researchers often assess key security using randomness tests or secret key capacity analysis. However, randomness tests can be misleading, as low-entropy inputs may still pass when obscured by hash functions or extractors. While key capacity captures the theoretical upper limit of secret bits from raw channels, it overlooks the impact of practical preprocessing. Recent studies have explored min-entropy as a more practical security metric. However, commonly used min-entropy estimators, such as those in NIST SP 800-90B (90B), may yield inaccurate results for complex sources, even overestimating entropy and thereby undermining security. Their applicability to PLKG also remains unverified. To address the above research gap, this paper focuses on modeling the received signal envelope, a common feature extracted for key generation. Leveraging typical wireless fading scenarios considered in key capacity analysis, we adopt the more general Weibull distribution to characterize the envelope statistics, which generalizes the Rayleigh model and better captures diverse propagation conditions. We first derive an explicit expression for the min-entropy in the absence of preprocessing, and then systematically analyze the impact of typical preprocessing operations on the statistical properties of the observed channel. Building on this foundation, we further establish theoretical upper and lower bounds on the min-entropy under representative preprocessing strategies. We further evaluate all 90B entropy estimators using both simulations and real-world channel measurements, comparing their estimates with our analytical results. While some estimators work well on raw data, they often fail or overestimate entropy after preprocessing, compromising security. In contrast, our conservative lower bound remains robust across all tested scenarios and preprocessing conditions. It offers a reliable fallback when standard estimators break down, providing a stronger theoretical foundation for secure key extraction in PLKG systems.
Physical and quantum random number generators (QRNGs) do not emit uniform bits: detector bias, dead time, after-pulsing, and environmental drift leave their raw output biased, correlated, and partially predictable to an adversary who models the device. Deployable generators therefore include a conditioning (randomness-extraction) stage that distils near-uniform keys from a source of certified min-entropy, and the cryptographic workhorse of that stage is the Leftover Hash Lemma (LHL). This paper analyses two ways in which real deployments depart from the textbook LHL. First, a physical source has no fixed min-entropy: continuous health tests, as standardised in NIST SP 800-90B, produce a runtime lower-bound estimate that varies from block to block and can occasionally be optimistic. Driving the extractor output length from this estimate, we prove an online adaptive extraction theorem whose security bound cleanly composes the estimator soundness error with the extractor error, yielding a (δ+ε)-secure key and separating how often the entropy estimate is optimistic from how uniform the hash output is, as two independently tunable budgets. Second, for settings that publish or reuse the extractor seed — public randomness beacons, reproducible pipelines, and seed-constrained devices — we prove that publishing the seed costs nothing, since this is the defining property of a strong extractor, and that reusing a single seed across m independent source blocks costs at most mε in statistical distance. A corollary bounds the security loss when the extracted key is consumed by a downstream symmetric primitive, and a worked instantiation with realistic QRNG parameters reports the achievable secret-key rate. A reproducible numerical simulation on a synthetic biased source corroborates all four claims. Throughout, we are explicit about the single load-bearing unproven premise — empirical min-entropy certification of the physical device — which belongs to entropy-source validation and lies outside the scope of the analysis.