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IEEE Transactions on Signal Processing

Generalized Channel Probing and Generalized Pre-Processing for Secret Key Generation

Yingbo Hua

Published Jan 1, 2023• 27 citations
Publisher

Abstract

The paper considers secret key generation (SKG) from a channel between Alice and Bob against Eve all with multiple antennas. A generalized channel probing (GCP) method and a generalized pre-processing (GPP) method are proposed as two major steps before quantization, information reconciliation and privacy amplification for SKG. The degree-of-freedom (DoF) of the secret key capacity (SKC), relative to power or signal-to-noise ratio, based on the data sets at Alice, Bob and Eve after the execution of GCP is shown. The SKC-DoF is also shown to be the same as that based the new data sets after the application of GPP except for modifications chosen for computational efficiency. In particular, if Eve has a less number of antennas than either Alice and/or Bob, the SKC-DoF grows with the number of random transmissions from either Alice and/or Bob within each channel coherence period. This SKC-DoF property for GCP and GPP does not require the reciprocity of the channel. A reciprocal channel simply adds a product of the numbers of antennas at Alice and Bob towards the total SKC-DoF. Also shown in this paper is GCP with embedded public pilots, which results in no loss of SKC-DoF. Two prior methods for channel probing are also reviewed, and it is shown that none of them has the above mentioned SKC-DoF property despite previous claims. The potential application areas of GCP and GPP include both wireless and wireline networks.

DOI
10.1109/tsp.2023.3259142

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The paper considers secret key generation (SKG) from a channel between Alice and Bob against Eve all with multiple antennas. A generalized channel probing (GCP) method and a generalized pre-processing (GPP) method are proposed as two major steps before quantization, information reconciliation and privacy amplification for SKG. The degree-of-freedom (DoF) of the secret key capacity (SKC), relative to power or signal-to-noise ratio, based on the data sets at Alice, Bob and Eve after the execution of GCP is shown. The SKC-DoF is also shown to be the same as that based the new data sets after the application of GPP except for modifications chosen for computational efficiency. In particular, if Eve has a less number of antennas than either Alice and/or Bob, the SKC-DoF grows with the number of random transmissions from either Alice and/or Bob within each channel coherence period. This SKC-DoF property for GCP and GPP does not require the reciprocity of the channel. A reciprocal channel simply adds a product of the numbers of antennas at Alice and Bob towards the total SKC-DoF. Also shown in this paper is GCP with embedded public pilots, which results in no loss of SKC-DoF. Two prior methods for channel probing are also reviewed, and it is shown that none of them has the above mentioned SKC-DoF property despite previous claims. The potential application areas of GCP and GPP include both wireless and wireline networks.
This paper presents a generalized channel probing (GCP) method and a generalized pre-processing (GPP) method as two consecutive frontend steps for secret key generation from a MIMO channel between two multi-antenna legitimate nodes against a multi-antenna eavesdropper (Eve). The degree of freedom (DoF) of the secret key capacity (SKC) of GCP/GPP are highlighted and discussed. If the number of antennas on Eve is larger than or equal to the larger number of the antennas on the two legitimate nodes, the SKC-DoF of GCP/GPP within each coherence period equals its minimum, which is either zero for non-reciprocal channel or the product of the numbers of antennas on the two legitimate nodes for reciprocal channel. Otherwise, the SKC-DoF of GCP/GPP increases with the number of random transmissions in GCP within each coherence period regardless of the channel reciprocity. A computational algorithm required for GPP is also discussed, and its performance illustrated via simulations.
This paper presents an analysis of Maurer-Ahlswede-Csiszar (MAC) bounds on secret-key capacity (SKC) based on a MIMO channel probing scheme between two multi-antenna nodes against a multi-antenna eavesdropper (Eve). To reveal useful insights into SKC, we consider the regime of high signal powers or high signal-to-noise ratios (SNR) where SKC consists of a first-order term (FoT) which is proportional to the logarithm of signal power, and a second-order term (SoT) which is invariant to signal power. Applying random-matrix theory for large numbers of antennas on all nodes, this paper shows the FoT and SoT of a lower MAC bound on SKC. While the FoT is consistent with prior work, which vanishes if none of the two nodes has more antennas than Eve and the channel is not perfectly reciprocal, the SoT shows a positive secrecy that increases linearly with the length of random transmissions from one node to another during channel probing even if Eve has many more antennas than the legitimate nodes.
This paper proposes a vector-quantization-based secret key generation (SKG) procedure to efficiently extract shared secret keys from correlated channel observations at two communicating terminals, Alice and Bob. Most existing SKG schemes utilize scalar quantization to extract secret key bits separately from each individual channel observation. This approach is simple to implement but yields higher key disagreement probability (or lower key entropy) compared with vector-quantization-based approaches. However, regardless of the quantizer design, quantization for SKG often suffers from the so-called cell-boundary problem, which occurs when the channel observations at Alice and Bob lie close to the quantization cell boundaries, resulting in high probability of key disagreement. In this paper, a general SKG procedure that utilizes sample and quantizer selection techniques to avoid this problem is first proposed. The vector quantizer adopted in the above procedure is designed by minimizing the quadratic distortion between the true channel vector and the noisy observation at Alice (or Bob). Then, by considering the case where the eavesdropper (Eve) may observe a channel vector that is correlated with that observed by Alice and Bob, a clustered key mapping scheme that assigns each secret key to multiple quantization cells in different clusters is also proposed to induce additional randomness at Eve and, thus, maintain high conditional key entropy. The effectiveness of the proposed schemes is demonstrated through computer simulations.
Secret key generation at physical layer has attracted more and more attentions as an emerging cryptography method. Compared to traditional security approaches, secret key generation at physical layer not only avoids the problem of key distribution, but also holds high efficiency and low complexity in application. Based on the principle of channel reciprocity, Alice and Bob both estimate the channel conditions and extract shared keys from this source of common randomness. In this paper, we first analyze the basic steps (channel estimation, sample quantization and key reconciliation) of secret key generation, key match rate with different quantization levels and key reconciliation times are also simulated. While in practical situations, different non-reciprocity factors affect the channel estimation step, key match rate can be greatly decreased and hardly meet real time cryptography requirements. In order to increase the key match rate, the unified framework of physical layer key generation has been extended to utilizing multiple observations of wireless channels to generate secret keys. An improved key generation approach with multiple observations can well deal with discrepancies between transceivers and keep increasing the key match rate. Theoretical analysis and simulation results both validate the significant improvement due to multiple observations. With an increased number of observations on both sides, the desired key match rate can be achieved much greater than with a single observation, and also the probability of key recovery by Eve can be decreased.
Physical layer secret key generation exploits the reciprocal channel randomness for key generation and has proven to be an effective addition security layer in wireless communications. However, static or scarcely random channels require artificially induced dynamics to improve the secrecy performance, e.g., using intelligent reflecting surface (IRS). One key challenge is that the induced random phase from IRS is also reflected in the direction to eavesdroppers (Eve). This leakage enables Eve nodes to estimate the legitimate channels and secret key via a globally known pilot sequence. To mitigate the secret key leakage issue, we propose to exploit random matrix theory to inform the design of a new physical layer secret key generation (PL-SKG) algorithm. We prove that, when sending appropriate random Gaussian matrices, the singular values of Alice's and Bob's received signals follow a similar probability distribution. Leveraging these common singular values, we propose a random Gaussian matrix based PL-SKG (RGM PL-SKG), which avoids the usages of the globally known pilot and thereby prevents the aforementioned leakage issue. Our results show the following: (i) high noise resistance which leads to superior secret key rate (SKR) improvement (up to 300%) in low SNR regime, and (ii) general improved SKR performance against multiple colluded Eves. We believe our combination of random matrix theory and PL-SKG shows a new paradigm to secure the wireless communication channels.