Physical layer approaches for generating secret encryption keys for wireless systems using channel information has attracted increased interest from researchers in recent years. This paper presents a new approach for secret key generation that is based on one-bit quantization of channel measurements along with log-likelihood ratios (LLRs) that are calculated based on the difference between channel estimates at legitimate reciprocal nodes. The proposed approach, which implements advantage distillation along with information reconciliation using Slepian-Wolf low-density parity check (LDPC) codes, is discussed and illustrated with numerical results obtained from simulations. These show the probability of bit disagreement for keys generated through the proposed approach and compare it with that of alternative methods for key generation based on channel state information.
Physical layer approaches for generating secret encryption keys for wireless systems using channel information has attracted increased interest from researchers in recent years. This paper presents a new approach for secret key generation that is based on one-bit quantization of channel measurements along with log-likelihood ratios (LLRs) that are calculated based on the difference between channel estimates at legitimate reciprocal nodes. The proposed approach, which implements advantage distillation along with information reconciliation using Slepian-Wolf low-density parity check (LDPC) codes, is discussed and illustrated with numerical results obtained from simulations. These show the probability of bit disagreement for keys generated through the proposed approach and compare it with that of alternative methods for key generation based on channel state information.
Physical layer approaches for generating secret encryption keys for wireless systems using channel information have attracted increased interest from researchers in recent years. This paper presents a new approach for calculating log-likelihood ratios (LLRs) for secret key generation that is based on one-bit quantization of channel measurements and the difference between channel estimates at legitimate reciprocal nodes. The studied secret key agreement approach, which implements advantage distillation along with information reconciliation using Slepian-Wolf low-density parity-check (LDPC) codes, is discussed and illustrated with numerical results obtained from simulations. These results show the probability of bit disagreement for keys generated using the proposed LLR calculations compared with alternative LLR calculation methods for key generation based on channel state information. The proposed LLR calculations are shown to be an improvement to the studied approach of physical layer secret key agreement.
The problem of key reconciliation based on Low-Density Parity-Check (LDPC) codes and Slepian-Wolf coding for physical layer key generation is investigated.When using the channel-state-information (CSI) of a reciprocal wireless channel for key generation between two legitimate users, independent noise components, quantization, and synchronization errors at the end nodes give rise to key differences that need to be corrected by sending side information.We provide a comparison of three different quantization schemes in terms of key disagreement rate and output probability distributions and present the loglikelihood formulations required by a soft decision LDPC decoder to perform key reconciliation, for the investigated quantization methods.
Secret key generation using channel measurements has emerged as a meaningful approach to enhance the security of a wireless link at the physical layer. This paper presents a novel approach to key generation based on precoding of the MIMO channel measurements, that does not require information on the channel statistics and has the ability to generate key bits at a rate that is independent of the channel coherence time. The proposed approach is illustrated with numerical results obtained from simulations showing the probability of bit disagreement between keys generated at transmitter/receiver side and Slepian-Wolf encoding with LDPC codes is used for key reconciliation.
Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.
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.
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.