Harvesting Physical-Layer Randomness in Millimeter Wave Bands

The unpredictability of the wireless channel has been used as a natural source of randomness to build physical-layer security primitives for shared key generation, authentication, access control, proximity verification, and other security properties. Compared to pseudo-random generators, it has the potential to achieve information-theoretic security. In sub-6 GHz frequencies, the randomness is harvested from the small-scale fading effects of RF signal propagation in rich scattering environments. However, the RF propagation characteristics follow sparse models with clustered paths when devices operate in millimeter-wave (mmWave) bands (5G and Next-Generation networks, Wi-Fi in 60GHz). Millimeter-wave transmissions are typically directional to increase the gain and combat high signal attenuation, leading to stable and more predictable channels. In this paper, we first demonstrate that state-of-the-art methods relying on channel state information or received signal strength measurements fail to produce high randomness. Accounting for the unique features of mmWave propagation, we propose a novel randomness extraction mechanism that exploits the random timing of channel blockage to harvest random bits. Compared with the prior art in CSI-based and context-based randomness extraction, our protocol remains secure against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive and active Man-in-the-Middle adversaries co-located with the legitimate devices</i>. We demonstrate the security properties of our method in a 28 GHz mmWave testbed in an indoor setting.

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Publication Details

Journal
IEEE Transactions on Mobile Computing
Published
2024-11-15
DOI
https://doi.org/10.1109/tmc.2024.3499876
Citations
2
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
0.42
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article

Harvesting Physical-Layer Randomness in Millimeter Wave Bands

Yanjun Pan, Loukas Lazos, Jingcheng Li, Ming Li et al.
2 citations
IEEE Transactions on Mobile Computing
Millimeter-Wave Propagation and Modeling
0.42
article

Harvesting Physical-Layer Randomness in Millimeter Wave Bands

Yanjun Pan, Loukas Lazos, Jingcheng Li, Ming Li, Ziqi Xu
article en
2 citations

Abstract

The unpredictability of the wireless channel has been used as a natural source of randomness to build physical-layer security primitives for shared key generation, authentication, access control, proximity verification, and other security properties. Compared to pseudo-random generators, it has the potential to achieve information-theoretic security. In sub-6 GHz frequencies, the randomness is harvested from the small-scale fading effects of RF signal propagation in rich scattering environments. However, the RF propagation characteristics follow sparse models with clustered paths when devices operate in millimeter-wave (mmWave) bands (5G and Next-Generation networks, Wi-Fi in 60GHz). Millimeter-wave transmissions are typically directional to increase the gain and combat high signal attenuation, leading to stable and more predictable channels. In this paper, we first demonstrate that state-of-the-art methods relying on channel state information or received signal strength measurements fail to produce high randomness. Accounting for the unique features of mmWave propagation, we propose a novel randomness extraction mechanism that exploits the random timing of channel blockage to harvest random bits. Compared with the prior art in CSI-based and context-based randomness extraction, our protocol remains secure against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive and active Man-in-the-Middle adversaries co-located with the legitimate devices</i>. We demonstrate the security properties of our method in a 28 GHz mmWave testbed in an indoor setting.

IEEE Transactions on Mobile ComputingVol. 24(3)
University of Arizona (US), University of Arkansas at Fayetteville (US)
Openalex Percentile: Top 37%
Millimeter-Wave Propagation and Modeling
0.42
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