Secret key generation based on channel reciprocity is an important branch of wireless physical layer security which provides strong or even theoretically perfect secret. With the pursuit of higher secret key rate, the time-frequency joint key extraction schemes have been recently considered, which can exploit simultaneously the resources contained in the frequency and time domains. However, the strong correlation existing between the broadband time-variant channel samples makes the decorrelation process indispensable before quantization. Due to the large size of these channel sample matrices, decorrelation methods based on full channel covariance matrix corresponding to high calculation complexity are difficult to deploy in realistic mobile communication systems. In this work, we propose a method which separates the decorrelation of time-frequency correlated channel characteristics into two steps. The performance of this proposed method is evaluated through both standardized channel model and practical measurements. It is shown that the decorrelation of samples with the two-step method could be close to that based on the full channel covariance matrix, as well as leading to a dramatical reduction on the calculation complexity. The impact of the signal-to-noise-ratio (SNR) on the secret key rate is analyzed, and no distinct difference is shown in this aspect between these two methods.
Secret key generation based on channel reciprocity is an important branch of wireless physical layer security which provides strong or even theoretically perfect secret. With the pursuit of higher secret key rate, the time-frequency joint key extraction schemes have been recently considered, which can exploit simultaneously the resources contained in the frequency and time domains. However, the strong correlation existing between the broadband time-variant channel samples makes the decorrelation process indispensable before quantization. Due to the large size of these channel sample matrices, decorrelation methods based on full channel covariance matrix corresponding to high calculation complexity are difficult to deploy in realistic mobile communication systems. In this work, we propose a method which separates the decorrelation of time-frequency correlated channel characteristics into two steps. The performance of this proposed method is evaluated through both standardized channel model and practical measurements. It is shown that the decorrelation of samples with the two-step method could be close to that based on the full channel covariance matrix, as well as leading to a dramatical reduction on the calculation complexity. The impact of the signal-to-noise-ratio (SNR) on the secret key rate is analyzed, and no distinct difference is shown in this aspect between these two methods.
As an alternative and appealing approach to providing information security in wireless communication systems, secret key generation at physical layer has demonstrated its potential in terms of efficiency and reliability over traditional cryptographic methods. Without the necessity of a management centre for key distribution or reliance on computational complexity, physical layer key generation protocols enable two wireless entities to extract identical and dynamic keys from the randomness of the wireless channels associated with them.\nIn this thesis, the reliability of secret key generation at the physical layer is examined in practical wireless channels with imperfect channel state information (CSI). Theoretical analyses are provided to relate key match rate with channel's signal-to-noise ratio (SNR), degrees of channel reciprocity, and iterations of information reconciliation.\nIn order to increase key match rate of physical layer secret key generation, improved schemes in the steps of channel estimation and sample quantization are proposed respectively. In the channel estimation step, multiple observations of the wireless channels are integrated with a linear processor to provide a synthesized and more accurate estimation of the wireless channel. In the sample quantization step, a magnitude based quantization method with two thresholds is proposed to quantize partial samples, where specific quantization areas are selected to reduce cross-over errors. Significant improvements in key match rate are proven for both schemes in theoretical analysis and numerical simulations. Key match rate can even achieve 100% in both schemes with the assistance of information reconciliation process.\nIn the end, a practical application of physical layer secret key generation is presented, where dynamic keys extracted from the wireless channels are utilized for securing secret data transmission and providing efficient access control.
Encryption of confidential data with a secret key has become a widespread technique for securing wireless transmissions. However, existing key distribution methods that either deliver the secret key with a key distribution center or exchange the secret key using public-key cryptosystems are unable to establish perfect secret keys necessary for symmetric encryption techniques. This research considers secret key establishment, under the broad research area of information theoretic security, using the reciprocal wireless channel as common randomness for the extraction of perfect secret keys in multiple-input multiple-output (MIMO)communication systems. The presentation discusses the fundamental characteristics of the time-variant MIMO wireless channel and establishes a realistic channel simulation model useful for assessing key establishment algorithms. Computational examples show the accuracy and applicability of the model. The discussion then turns to an investigation of the influence of the spatial and temporal correlation of the channel coefficients on the bound of the key size generated from the common channel, and it is found that a sampling approach exists that can generate a key using the minimum sampling time. A practical key generation protocol is then developed based on an enhancement of a published channel coefficient quantization method that incorporates flexible quantization levels, public transmission of the correlation eigenvector matrix and low-density parity-check (LDPC) coding to improve key agreement. This investigation leads to the development of improved channel quantization techniques that dynamically shift the quantization boundaries at one node based on the information provided by the other node. Analysis based on a closed-form bound for the key error rate (KER) and simulations based on the channel model and measurement data show that the enhanced algorithms are able to dramatically reduce key mismatch and asymptotically approach the KER bound. Finally, other secret key generation algorithms based on channel-encryption rather than quantization are discussed, leading to a new concept for secret key generation using the common wireless channel.
Wireless secret key generation has emerged as a promising technique for Internet-of-Things (IoT) systems to establish shared encryption keys between the server and legitimate mobile user. This article focuses on the use of multidomain joint information to achieve a high key generation rate (KGR) and the implementation of a reliable, low-complexity secret key generation mechanism for distributed antenna systems (DAS) with orthogonal-frequency division multiplexing (OFDM). We present a space-time-frequency channel state information (CSI)-based key generation scheme based on a two-step approach of adaptive link selection and stepwise decorrelation algorithms. The performance is evaluated in terms of KGR, key disagreement rate (KDR), randomness, and computational complexity by using both a standardized channel model and real-world measurements. Numerical results show that our proposed low-complexity algorithms effectively utilize the space-time-frequency CSI to multiply the KGR in both indoor and outdoor environments. Through adaptive link selection in DAS, the KDR is maintained within a correctable range, thereby ensuring the validity of generated keys in dynamic environments. Further applying stepwise decorrelation reduces the computational complexity by more than half while satisfying all eight key generation randomness tests in the NIST test suite.
Providing secure communication links between devices of low computational power has been increasingly investigated during recent years. The need for fast and easy-to-implement security for computationally weak wireless devices has lead to the development of physical layer based key generation approaches. The generation of symmetric cryptographic keys out of wireless channel properties turned out to be a promising approach comprising advantages of symmetric as well as asymmetric cryptography. Numerous quantization schemes have been proposed in previous works to increase the Key Generation Rate (KGR). Also by increasing the sampling rate, the input data can be generated faster. However, due to fast sampling rates, redundancy of subsequent bits will increase. This lowers the quality, that is, the randomness of the generated secret key and makes the system more vulnerable to brute-force attacks. We present and analyze different techniques for transforming a temporally correlated sequence into a compressed sequence of decorrelated bits and, therefore, assure non-redundant key sequences.
Standard cryptography is expected to poorly fit IoT applications and services, as IoT devices can hardly cope with the computational complexity often required to run encryption algorithms. In this framework, physical layer security is often claimed as an effective solution to enforce secrecy in IoT systems. It relies on wireless channel characteristics to provide a mechanism for secure communications, with or even without cryptography. Among the different possibilities, an interesting solution aims at exploiting the random-like nature of the wireless channel to let the legitimate users agree on a secret key, simultaneously limiting the eavesdropping threat thanks to the spatial decorrelation properties of the wireless channel. The actual reliability of the channel-based key generation process depends on several parameters, as the actual correlation between the channel samples gathered by the users and the noise always affecting the wireless communications. The sensitivity of the key generation process can be expressed by the secrecy key rate, which represents the maximum number of secret bits that can be achieved from each channel observation. In this work, the secrecy key rate value is computed by means of simulations carried out under different working conditions in order to investigate the impact of major channel parameters on the SKR values. In contrast to previous works, the secrecy key rate is computed under a line-of-sight wireless channel and considering different correlation levels between the legitimate users and the eavesdropper.
Physical layer characteristics of a wireless channel, which can be measured independently by the two communicating parties and yield near-identical results due to the channel reciprocity property, have been shown to be an important source of shared secrecy generation. Various types of channel state information (CSI), such as the received signal strength (RSS) and the channel impulse response (CIR) can be utilized for this purpose. Through periodic probing of the CSI, a continuous cycle of secret bit generation and key renewal can be maintained. However, many forms of wireless CSI inherently have substantial temporal correlation, which may hinder the secrecy generation process. Accordingly, various autocorrelation reduction (decorrelation) methods have been proposed, using either sub-sampling approaches that discard potentially valuable CSI data, or, computationally expensive transform domain approaches such as the Discrete Cosine Transform (DCT), Karhunen–Loeve Transform (KLT), and principal component analysis (PCA) that may not be feasible or desirable for resource and energy constrained devices. This paper proposes a low-complexity method for reducing the autocorrelation of the CSI measurements through a reordering of the data based on integer sequences such as the Fibonacci sequences, and applies it to various types of CSI data that represent both the individual path level CIR and the aggregate level multipath gain or RSS. We evaluate the performance of the method in three standard multipath ITU channel models. Fibonacci sequences are observed to be an effective means of decorrelating the channel measurement data, thereby eliminating the need for computationally intensive methods.