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On the Role of Artificial Noise in Training and Data Transmission for Secret Communications
[article]
2015
arXiv
pre-print
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This work considers the joint design of training and data transmission in physical-layer secret communication systems, and examines the role of artificial noise (AN) in both of these phases. ...
Then, by maximizing the approximate achievable secrecy rate, the optimal power allocation between signal and AN in both training and data transmission phases is obtained for both conventional and AN-assisted ...
Different from previous works in the literature that focus on either training or data transmission, we consider the joint design and examine the role of AN in both of these phases. ...
arXiv:1511.01791v1
fatcat:5zjb63cdxffvdpiqbixjj65v5a
Subcarrier Pairing and Power Allocation With Interference Management in Cognitive Relay Networks Based on Genetic Algorithms
2016
IEEE Transactions on Vehicular Technology
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Peter Hong, "On the Role of Artificial Noise in Training and Data Transmission for Secret Communications", IEEE Transactions on Information Forensics and Security, vol. 12, no. 3, pp. 516 -531, Mar. 2017 ...
Lin and H.-J. Su, "On Secrecy Rate of the Generalized Artificial-Noise Assisted Secure Beamforming for Wiretap Channels," IEEE Journal on Selected Areas in Communications, vol. 31. ...
doi:10.1109/tvt.2015.2483199
fatcat:3tqk6mh5rnaunolajoxwmcdi5m
An Overview of Physical Layer Security in Wireless Communication Systems With CSIT Uncertainty
2016
IEEE Access
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In practical wireless communication systems, CSIT is usually obtained, prior to data transmission, through CSI feedback sent by the receivers. ...
The concept of physical layer security builds on the pivotal idea of turning the channel's imperfections, such as noise and fading, into a source of security. ...
Goel and Negi introduced the artificial noise technique in [27] and [28] . The main idea of the work is to exploit part of the available transmission power to send artificially generated noise. ...
doi:10.1109/access.2016.2612585
fatcat:lhpjsjnkl5gg7oqjticfmww3x4
Adaptive limited feedback for MISO wiretap channels with cooperative jamming
2013
2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
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This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret ...
The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. ...
The role of artificial noise for jamming eavesdroppers in wiretap broadcast channels was investigated in [93] - [95] . ...
doi:10.1109/spawc.2013.6612057
dblp:conf/spawc/PeiMW13
fatcat:gjn4o2l4qjae5ipsr6zfjo35yu
Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey
2014
IEEE Communications Surveys and Tutorials
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This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret ...
The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. ...
The role of artificial noise for jamming eavesdroppers in wiretap broadcast channels was investigated in [93] - [95] . ...
doi:10.1109/surv.2014.012314.00178
fatcat:cmqxgwotmvbmldwqdvjkzsweam
Secured Image Transmission Using a Novel Neural Network Approach and Secret Image Sharing Technique
2015
International Journal of Signal Processing, Image Processing and Pattern Recognition
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In this secret image is sent on the unsecured communication channel [16] is first embedded with a cover image and then the stego image is divided into shares. ...
At the receiving end, decryption of the encrypted shares are done using an artificial neural network and hence eliminating the need of key exchange before the transmission of data which is a prerequisite ...
Secure transmission of data is needed in the worldwide computer network environment. ...
doi:10.14257/ijsip.2015.8.1.16
fatcat:xurtydrpbrgqnnjv4y53nlouee
Secret Channel Training to Enhance Physical Layer Security With a Full-Duplex Receiver
2018
IEEE Transactions on Information Forensics and Security
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Based on our analysis, the optimal power allocation between CT and data/AN transmission at the legitimate transmitter/receiver is determined. ...
Index Terms-Physical layer security, channel training, full duplex, artificial noise, power allocation. ...
In addition, [25] investigated the role of AN in both the channel training and data transmission in physical-layer secret communications, where the AN is used to prevent Eve from estimating the eavesdropper's ...
doi:10.1109/tifs.2018.2834301
fatcat:5x2j52rgzzg47m2xi5ccb7soua
On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence
[article]
2023
arXiv
pre-print
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We then address the future role of model compression methods in the research towards secret sharing on DNNs with state-of-the-art performance. ...
Deep Neural Network (DNN) Inference in Edge Computing, often called Edge Intelligence, requires solutions to insure that sensitive data confidentiality and intellectual property are not revealed in the ...
The client is responsible for noising and de-noising, which can be computationally expensive depending on the scheme used (e.g., auto-encoders and decoders for obfuscation). ...
arXiv:2302.05323v2
fatcat:dgidl34eg5frtbtmotcodo74mu
Federated Learning: A Signal Processing Perspective
[article]
2021
arXiv
pre-print
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in the areas of signal processing and communications. ...
Consequently, dedicated schemes derived from these areas are expected to play an important role in the success of federated learning and the transition of deep learning from the domain of centralized servers ...
In general, encryption methods involve additional communications overhead, as well as possible secret key sharing between the users [33] . 2) Artificial noise implies that each user perturbs its local ...
arXiv:2103.17150v2
fatcat:pktgiqowsjbklfnj753ehdbnhu
Evaluation, Energy Optimization, and Spectrum Analysis of an Artificial Noise Technique to Improve CWSN Security
2013
International Journal of Distributed Sensor Networks
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This paper presents the security evaluation, energy consumption optimization, and spectrum scarcity analysis of artificial noise techniques to increase physical-layer security in Cognitive Wireless Sensor ...
Both are affected because the number of packets transmitted by the network and the active period of the nodes increase. ...
The sleep mode consumption
Cooperative Artificial Noise Scheme The key idea in this paper is that a transmitter, in cooperation with helper nodes, can artificially generate noise to conceal the secret ...
doi:10.1155/2013/834547
fatcat:2jhlxvj22vef3ifiiblhnjuija
Secure Continuous-Variable Quantum Key Distribution with Machine Learning
2021
Photonics
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In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. ...
Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied. ...
All in all, machine learning has gradually played an important role in the quantum safe encryption transmission for real CV systems, paving the way for secure QKD with realistic devices. ...
doi:10.3390/photonics8110511
fatcat:o2gmbck4nfbevfydzvgjduhuni
A Survey of the Various Steganography Techniques Using Soft Computing Techniques
2021
International Journal for Research in Applied Science and Engineering Technology
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Steganography aims at communicating the secret data in an appropriate multimedia carrier. In this paper, the various techniques used to perform Steganography in a secure way are studied and reviewed. ...
In this the various Artificial Intelligence techniques used for steganography are reviewed and analyzed. ...
So, NNs trained is the secret key. Then, we use NNs trained to classify pixels, and select pixels in less sensitive areas to embed more secret data. ...
doi:10.22214/ijraset.2021.36667
fatcat:aosipzwlwbdazhkqicrfvdrcku
Development of Novel Digital Equalizers for Noisy Nonlinear Channel using Artificial Immune System
2008
2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems
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Transmission and storing of high density digital information plays an important role in the present age of communication and information technology. ...
Simulation study has been carried out to show superior performance of the proposed equalizer particularly for nonlinear noisy channels compared to that offered by LMS and GA based training. ...
Touri et al have developed [10] deterministic worst case frame work for perfect reconstruction of discrete data transmission through a dispersive communication channel. ...
doi:10.1109/iciinfs.2008.4798337
fatcat:kxttxqbss5eobowh5fxgccr4d4
Information Security of PHY Layer in Wireless Networks
2016
Journal of Sensors
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Along the way, we analyze the pros and cons of current secure technologies in each category. ...
In this paper, we firstly identify and summarize the threats and vulnerabilities in PHY layer of wireless networks. ...
Acknowledgments This work is partially supported by the National Natural Sci- ...
doi:10.1155/2016/1230387
fatcat:lqudc242pvgepggbkzuzbuk2y4
Wireless Communication, Sensing, and REM: A Security Perspective
2021
IEEE Open Journal of the Communications Society
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This includes ensuring no illegitimate access to the information/transmission is allowed, the data shared between legitimate nodes is accurate, and there is no disruption of the services offered by the ...
A case study to highlight the need for a secure REM in the context of V2X communication is described in Section VII. ...
The transmission of artificial noise or interfering signals in different directions and beamwidths is based on the location of legitimate nodes, the geometry of the nodes, AoA, AoD, and the level of required ...
doi:10.1109/ojcoms.2021.3054066
fatcat:klhorbflvvdrlkqyndctn3lwtq
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