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IEEE Vehicular Technology Conference

Secret Key Generation Rates over Frequency Selective Channels

Miroslav Mitev, Andre Noll Barreto, Thuy M. Pham, Gerhard Paul Fettweis

Published Jun 1, 2022• 10 citations
Publisher

Abstract

The emergence of Internet of things (IoT) applications brings the challenge of finding lightweight schemes for secret key distribution. A promising solution coming from the physical layer is the so called secret key generation (SKG) from shared randomness. SKG allows two communicating parties to extract the shared randomness already present in wireless channels. This work investigates the achievable SKG rates using received signal strength (RSS) as a key generation parameter. A multi-path scenario is considered and the probability density function of the RSS in different channel conditions is evaluated. Next, through a numerical evaluation, the SKG rates are derived in the form of mutual information (MI) estimates, using a 3GPP standard channel model. The simulations are performed for different values of bandwidth and delay spread. We demonstrate that while both parameters have similar impact on how multi-path components are resolved, their effect on the MI is opposite.

DOI
10.1109/vtc2022-spring54318.2022.9860555

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The emergence of Internet of things (IoT) applications brings the challenge of finding lightweight schemes for secret key distribution. A promising solution coming from the physical layer is the so called secret key generation (SKG) from shared randomness. SKG allows two communicating parties to extract the shared randomness already present in wireless channels. This work investigates the achievable SKG rates using received signal strength (RSS) as a key generation parameter. A multi-path scenario is considered and the probability density function of the RSS in different channel conditions is evaluated. Next, through a numerical evaluation, the SKG rates are derived in the form of mutual information (MI) estimates, using a 3GPP standard channel model. The simulations are performed for different values of bandwidth and delay spread. We demonstrate that while both parameters have similar impact on how multi-path components are resolved, their effect on the MI is opposite.
In this paper, the feasibility of implementing a lightweight key distribution scheme using physical layer security for secret key generation (SKG) is explored. Specifically, we focus on examining SKG with the received signal strength (RSS) serving as the primary source of shared randomness. Our investigation centers on a frequency-selective line-of-sight (LoS) multipath channel, with a particular emphasis on assessing SKG rates derived from the distributions of RSS. We derive the received signal distributions based on how the multipath components resolve at the receiver. The mutual information (MI) is evaluated based on LoS 3GPP channel models using a numerical estimator. We study how the bandwidth, delay spread, and Rician K-factor impact the estimated MI. This MI then serves as a benchmark setting bounds for the SKG rates in our exploration.
The sixth generation of wireless networks (6G) is expected to support the deployment of Internet of things (IoT) devices in massive scales. Finding lightweight and decentralized secret-key distribution primitives is therefore a challenge. Secret-key generation (SKG) from wireless channel coefficients is seen as a possible solution. It allows the extraction of secret keys using the channel randomness observed at the physical layer, without a centralized key distribution server. In this work an SKG approach suitable for wideband IoT devices is proposed. We investigate a filterbank-based SKG method, in which secret bits are generated through power measurements over different frequencies. To minimize dependencies and correlation among frequencies the quantile and the Karhunen-Loeve transforms are used. Finally, we perform a numerical evaluation of the achievable SKG rates, in the form of mutual-information (MI) estimates, using 3GPP channel models. Our numerical evaluation shows that the achievable SKG rate depends on the channel statistics, and, hence, to optimally harvest the information, devices need to be channel-aware.
Physical layer security (PLS) techniques hold promise for augmenting secure communications in the 5thgeneration of mobile wireless networks. Secret key generation (SKG) is a PLS technique which exploits the wireless propagation channel's randomness to generate symmetric key bits for information encryption and decryption. This work proposes symmetric key generation based on non-uniform quantization of the received signal strength (RSS) samples of a Nakagami-mmm fading channel. The proposed strategy for non-uniform quantization for SKG aims to set quantization thresholds for maximal key randomness and high values of key generation rate (KGR) and key agreement probability (KAP). Finally, a framework is proposed to use single node RSS measurements, readily available in the literature, to generate RSS samples at the other link end. This framework facilitates the testing of new SKG algorithms that require simultaneous RSS measurements by the legitimate nodes, which are not readily available in the open literature. The effectiveness of the proposed SKG scheme is validated through Monte Carlo methods and the National Institute of Standards and Technology (NIST) test suite for assessing the randomness of the generated key sequence.
The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel’s coherence time. This work acknowledges the fact that the CSI consists of deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, espectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.
The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel's coherence time. This work acknowledges the fact that the CSI consists of deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, respectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.
The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel's coherence time. This work acknowledges the fact that the CSI consists of deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, respectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.