Soft-metric-based information reconciliation techniques for QKD

2010 ◽  
Author(s):  
Marina Mondin ◽  
Fred Daneshgaran ◽  
Maria Delgado ◽  
Fabio Mesiti
Keyword(s):  
Information Reconciliation

Quantization of high dimensional Gaussian vector using permutation modulation with application to information reconciliation in continuous variable QKD

2017 ◽  
Vol 15 (08) ◽  
pp. 1740028 ◽  
Author(s):  
Fred Daneshgaran ◽  
Marina Mondin ◽  
Khashayar Olia
Keyword(s):  
Error Correction ◽  
Error Probability ◽  
Forward Error Correction ◽  
Continuous Variable ◽  
Unequal Error Protection ◽  
Secret Key ◽  
Information Reconciliation ◽  
Low Snr ◽  
Similar Sign ◽  
Sign Error

This paper is focused on the problem of Information Reconciliation (IR) for continuous variable Quantum Key Distribution (QKD). The main problem is quantization and assignment of labels to the samples of the Gaussian variables observed at Alice and Bob. Trouble is that most of the samples, assuming that the Gaussian variable is zero mean which is de-facto the case, tend to have small magnitudes and are easily disturbed by noise. Transmission over longer and longer distances increases the losses corresponding to a lower effective Signal-to-Noise Ratio (SNR) exasperating the problem. Quantization over higher dimensions is advantageous since it allows for fractional bit per sample accuracy which may be needed at very low SNR conditions whereby the achievable secret key rate is significantly less than one bit per sample. In this paper, we propose to use Permutation Modulation (PM) for quantization of Gaussian vectors potentially containing thousands of samples. PM is applied to the magnitudes of the Gaussian samples and we explore the dependence of the sign error probability on the magnitude of the samples. At very low SNR, we may transmit the entire label of the PM code from Bob to Alice in Reverse Reconciliation (RR) over public channel. The side information extracted from this label can then be used by Alice to characterize the sign error probability of her individual samples. Forward Error Correction (FEC) coding can be used by Bob on each subset of samples with similar sign error probability to aid Alice in error correction. This can be done for different subsets of samples with similar sign error probabilities leading to an Unequal Error Protection (UEP) coding paradigm.

QKD Iterative Information Reconciliation Based on LDPC Codes

2020 ◽  
Vol 59 (6) ◽  
pp. 1717-1729
Author(s):  
Guo Limei ◽  
Ran Qi ◽  
Jin Di ◽  
Huang Duan
Keyword(s):  
Ldpc Codes ◽  
Information Reconciliation

scholarly journals A Simple Secret Key Generation by Using a Combination of Pre-Processing Method with a Multilevel Quantization

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 192 ◽  
Author(s):  
Mike Yuliana ◽  
Wirawan ◽  
Suwadi
Keyword(s):  
Physical Layer Security ◽  
Processing Method ◽  
Communication Security ◽  
Key Generation ◽  
Secret Key ◽  
Secret Key Generation ◽  
Information Reconciliation ◽  
Security Challenges ◽  
Iot Devices ◽  
Multilevel Quantization

Limitations of the computational and energy capabilities of IoT devices provide new challenges in securing communication between devices. Physical layer security (PHYSEC) is one of the solutions that can be used to solve the communication security challenges. In this paper, we conducted an investigation on PHYSEC which utilizes channel reciprocity in generating a secret key, commonly known as secret key generation (SKG) schemes. Our research focused on the efforts to get a simple SKG scheme by eliminating the information reconciliation stage so as to reduce the high computational and communication cost. We exploited the pre-processing method by proposing a modified Kalman (MK) and performing a combination of the method with a multilevel quantization, i.e., combined multilevel quantization (CMQ). Our approach produces a simple SKG scheme for its significant increase in reciprocity so that an identical secret key between two legitimate users can be obtained without going through the information reconciliation stage.

Information reconciliation through an agent-controlled graph model

2020 ◽  
Vol 24 (18) ◽  
pp. 14019-14037
Author(s):  
Ramzi A. Haraty ◽  
Rita Saba
Keyword(s):  
Graph Model ◽  
Information Reconciliation
Sign in / Sign up

Export Citation Format

Share Document