As wireless communication scenarios grow more complicated, security issues are becoming increasingly prominent and severe. In the Internet of Things and vehicle-to-everything scenarios, conventional cryptographic technology faces numerous challenges. These include difficulties in secret key distribution and management, low update rates of secret keys, and vulnerability to quantum attacks. Physical layer secret key generation is considered a promising solution to security issues. The perfect secrecy proposed by Shannon can be achieved by combining secret key generation and the one-time pad when the length of secret keys is equal to that of plaintext. Hence, it is important to increase secret key generation rates. Intelligent reflecting surfaces demonstrate great advantages in improving the secret key generation performance. This paper provides a comprehensive review of current research efforts related to secret key generation assisted by intelligent reflecting surfaces, which is divided into three main categories: introducing the randomness of intelligent reflecting surfaces, optimizing the reflecting coefficients, and designing probing protocols. Comparative results of existing optimization approaches are provided and discussed. Furthermore, we emphasize the significance of selecting a random source of secret key generation from the perspective of information theory. Finally, two significant application scenarios, the Industrial Internet of Things and vehicle-to-everything, are discussed, and some challenges and opportunities are presented.
As wireless communication scenarios grow more complicated, security issues are becoming increasingly prominent and severe. In the Internet of Things and vehicle-to-everything scenarios, conventional cryptographic technology faces numerous challenges. These include difficulties in secret key distribution and management, low update rates of secret keys, and vulnerability to quantum attacks. Physical layer secret key generation is considered a promising solution to security issues. The perfect secrecy proposed by Shannon can be achieved by combining secret key generation and the one-time pad when the length of secret keys is equal to that of plaintext. Hence, it is important to increase secret key generation rates. Intelligent reflecting surfaces demonstrate great advantages in improving the secret key generation performance. This paper provides a comprehensive review of current research efforts related to secret key generation assisted by intelligent reflecting surfaces, which is divided into three main categories: introducing the randomness of intelligent reflecting surfaces, optimizing the reflecting coefficients, and designing probing protocols. Comparative results of existing optimization approaches are provided and discussed. Furthermore, we emphasize the significance of selecting a random source of secret key generation from the perspective of information theory. Finally, two significant application scenarios, the Industrial Internet of Things and vehicle-to-everything, are discussed, and some challenges and opportunities are presented.
Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for the wireless communication system. However, in a static environment, the secret key rate is low due to the lack of channel time-variation. To solve this problem, this letter proposes a novel secret key generation scheme assisted by an intelligent reflecting surface with discrete phase shifts. In the scheme, legitimate nodes construct the dynamic time-varying channel by rapidly and randomly switching the phase of IRS elements. Then, channel coefficients are used to generate the secret key. Based on the IRS channel model, we derive the probability distribution of the channel coefficient and the expressions of the secret key rate. Furthermore, we optimize the secret key rate by adjusting the switching time of the IRS phase. Monte Carlo simulation and numerical results show that our proposed scheme can update the secret key in a static environment.
Physical layer secret key generation (PHYSKG) is a promising technology to provide lightweight and information-theoretically secure key sharing. It has been widely studied in wireless communications and the Internet of Things (IoT). Yet, this technology is nontrivial for large scale wireless networks due to high overhead, insufficient security, and poor adaptability. Recently, artificial intelligence (AI)-based PHYSKG has shown greater performance and higher security with lower overhead than traditional schemes. Although many existing works have been devoted to exploiting AI-based PHYSKG, a scientific review is still missing, but highly anticipated in the literature. In this article, we perform a serious review on AI-based PHYSKG in wireless communications. After a comprehensive introduction to the processing procedure of AI-based PHYSKG, we propose a set of criteria for justifying its performance with regard to key generation efficiency, reliability, security, and generality. Based on these criteria, we review and analyze the state-of-art, focusing on randomness extraction and quantification. On the basis of the review, we further indicate open issues, current challenges, and future research direction in this field.
Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single-Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with existing user-introduced randomness schemes, the proposed scheme can maintain good performance as the number of eavesdropping nodes increases. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas, and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.
Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single-Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with existing user-introduced randomness schemes, the proposed scheme can maintain good performance as the number of eavesdropping nodes increases. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas, and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.
With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings.
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.