skip to main content
10.1145/3395352.3402654acmconferencesArticle/Chapter ViewAbstractPublication PageswisecConference Proceedingsconference-collections
short-paper

Deep learning based wiretap coding via mutual information estimation

Published: 16 July 2020 Publication History

Abstract

Recently, deep learning of encoding and decoding functions for wireless communication has emerged as a promising research direction and gained considerable interest due to its impressive results. A specific direction in this growing field are neural network-aided techniques that work without a fixed channel model. These approaches utilize generative adversarial networks, reinforcement learning, or mutual information estimation to overcome the need of a known channel model for training. This paper focuses on the last approach and extend it to secure channel coding schemes by sampling the legitimate channel and additionally introduce security constraints for communication. This results in a mixed optimization between the mutual information estimate, the reliability of the code and its secrecy constraint. It is believed that this lays the foundation for flexible, generalizable physical layer security approaches due to its independence of specific model assumptions.

References

[1]
Martín Abadi, Ashish Agarwal, Paul Barham, etal. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/ Software available from tensorflow.org.
[2]
Fayçal A. Aoudia and Jakob Hoydis. 2018. End-to-End Learning of Communications Systems Without a Channel Model. In Proc. 52nd Asilomar Conf. Signals, Systems, and Computers. Pacific Grove, CA, 298--303.
[3]
David Barber and Felix V. Agakov. 2003. The IM Algorithm: A Variational Approach to Information Maximization. In Proc. Adv. Int. Conf. Neural Inf. Process. Syst. Vancouver and Whistler, Canada, 201--208.
[4]
Mohamed I. Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual Information Neural Estimation. In Proc. of the 35th Int. Conf. on Machine Learning, Vol. 80. Stockholm, Sweden, 531--540.
[5]
Karl-Ludwig Besser, Pin-Hsun Lin, Carsten R. Janda, and Eduard A. Jorswieck. 2020. Wiretap Code Design by Neural Network Autoencoders. IEEE Trans. Inf. Forensics Security 15 (2020), 3374--3386.
[6]
Matthieu Bloch and João Barros. 2011. Physical-Layer Security: From Information Theory to Security Engineering. Cambridge University Press, Cambridge, UK.
[7]
Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information Theory (2 ed.). Wiley & Sons.
[8]
Georges A. Darbellay and Igor Vajda. 1999. Estimation of the Information by an Adaptive Partitioning of the Observation Space. IEEE Trans. Inf. Theory 45, 4 (May 1999), 1315--1321.
[9]
Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis, and Stephan ten Brink. 2018. Deep Learning Based Communication Over the Air. IEEE J. Sel. Topics Signal Process. 12, 1 (Feb. 2018), 132--143.
[10]
Timothy Dozat. 2016. Incorporating Nesterov Momentum into Adam. In Proc. 4th Int. Conf. Learning Representations Workshops. San Juan, Puerto Rico.
[11]
Andrew M. Fraser and Harry L. Swinney. 1986. Independent Coordinates for Strange Attractors from Mutual Information. Phys. Rev. A 33, 2 (Feb. 1986), 1134.
[12]
Rick Fritschek. 2020. Simulations WiseML (2020). https://github.com/Fritschek.
[13]
Rick Fritschek, Rafael F. Schaefer, and Gerhard Wunder. 2019. Deep Learning for Channel Coding via Neural Mutual Information Estimation. In Proc. 20th Int. Workshop Signal Process. Adv. Wireless Commun. Cannes, France, 1--5.
[14]
Rick Fritschek, Rafael F. Schaefer, and Gerhard Wunder. 2019. Deep Learning for the Gaussian Wiretap Channel. In Proc. IEEE Int. Conf. Commun. Shanghai, China, 1--6.
[15]
Rick Fritschek, Rafael F. Schaefer, and Gerhard Wunder. 2020. Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison. In Proc. 21th Int. Workshop Signal Process. Adv. Wireless Commun. Atlanta, GA, USA.
[16]
Shuyang Gao, Greg Ver Steeg, and Aram Galstyan. 2015. Efficient Estimation of Mutual Information for Strongly Dependent Variables. In Proc. 18th Int. Conf. Artificial Intelligence and Statistics. San Diego, 277--286.
[17]
Weihao Gao, Sewoong Oh, and Pramod Viswanath. 2018. Demystifying Fixed k-Nearest Neighbor Information Estimators. IEEE Trans. Inf. Theory 64, 8 (Aug. 2018), 5629--5661.
[18]
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Proc. Adv. Neural Inf. Process. Syst. Montréal, Canada, 2672--2680.
[19]
Mathieu Goutay, Fayçal A. Aoudia, and Jakob Hoydis. 2018. Deep Reinforcement Learning Autoencoder with Noisy Feedback. arXiv preprint arXiv:1810.05419 (Oct. 2018).
[20]
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (Dec. 2014).
[21]
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. 2004. Estimating Mutual Information. Phys. Rev. E 69, 6 (June 2004), 066138.
[22]
S. Leung-Yan-Cheong and M.E. Hellman. 1978. The Gaussian Wire-tap Channel. IEEE Trans. Inf. Theory 24, 4 (July 1978), 451--456.
[23]
Thomas Marchioro, Nicola Laurenti, and Deniz Güdüz. 2020. Adversarial Networks for Secure Wireless Communications. In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process. Barcelona, Spain, 8748--8752.
[24]
Ueli M. Maurer and Stefan Wolf. 2000. Information-Theoretic Key Agreement: From Weak to Strong Secrecy for Free. In EUROCRYPT 2000, Lecture Notes in Computer Science, Vol. 1807. Springer-Verlag, 351--368.
[25]
XuanLong Nguyen, Martin J. Wainwright, and Michael I. Jordan. 2010. Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization. IEEE Trans. Inf. Theory 56, 11 (Nov. 2010), 5847--5861.
[26]
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-GAN: Training Generative Neural Samplers Using Variational Divergence Minimization. In Proc. Adv. Neural Inf. Process. Syst. Barcelona, Spain, 271--279.
[27]
Frédérique Oggier, Patrick Solé, and Jean-Claude Belfiore. 2016. Lattice Codes for the Wiretap Gaussian Channel: Construction and Analysis. IEEE Trans. Inf. Theory 62, 10 (Oct. 2016), 5690--5708.
[28]
Timothy O'Shea and Jakob Hoydis. 2017. An Introduction to Deep Learning for the Physical Layer. IEEE Trans, on Cogn. Commun. Netw. 3, 4 (Dec. 2017), 563--575.
[29]
Timothy J. O'Shea, Tamoghna Roy, Nathan West, and Benjamin C. Hilburn. 2018. Physical Layer Communications System Design Over-the-Air Using Adversarial Networks. In Proc. 26th European Signal Process. Conf. Rome, Italy, 529--532.
[30]
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A. Alemi, and George Tucker. 2018. On Variational Lower Bounds of Mutual Information. In Proc. Adv. Neural Inf. Process. Syst. Workshop on Bayesian Deep Learning. Montréal, Canada.
[31]
Avraham Ruderman, Mark Reid, Dario Garcia-Garcia, and James Petterson. 2012. Tighter Variational Representations of f-Divergences via Restriction to Probability Measures. arXiv preprint arXiv:1206.4664 (June 2012).
[32]
Taiji Suzuki, Masashi Sugiyama, Jun Sese, and Takafumi Kanamori. 2008. Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. In New Challenges for Feature Selection in Data Mining and Knowledge Discovery. Antwerp, Belgium, 5--20.
[33]
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation Learning with Contrastive Predictive Coding. arXiv preprint arXiv:1807.03748 (July 2018).
[34]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.
[35]
Aaron D. Wyner. 1975. The Wire-Tap Channel. Bell Syst. Tech. J. 54 (Oct. 1975), 1355--1387.
[36]
Hao Ye, Geoffrey Y. Li, Biing-Hwang F. Juang, and Kathiravetpillai Sivanesan. 2018. Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN. In Proc. IEEE Global Commun. Conf. Workshops. Abu Dhabi, UAE, 1--5.
[37]
Xinliang Zhang and Mojtaba Vaezi. 2019. Deep Learning based Precoding for the MIMO Gaussian Wiretap Channel. In Proc. IEEE Global Commun. Conf. Workshops. Waikoloa, HI, USA.

Cited By

View all
  • (2024)Coding Theory Advances in Physical‐Layer SecrecyPhysical‐Layer Security for 6G10.1002/9781394170944.ch2(19-42)Online publication date: 25-Oct-2024
  • (2023)Practical Wiretap Code Design by Concatenated Autoencoder and LDPC Codes2023 IEEE International Workshop on Information Forensics and Security (WIFS)10.1109/WIFS58808.2023.10375067(1-7)Online publication date: 4-Dec-2023
  • (2023)Benchmarking Neural Capacity Estimation: Viability and ReliabilityIEEE Transactions on Communications10.1109/TCOMM.2023.325525171:5(2654-2669)Online publication date: May-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WiseML '20: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning
July 2020
91 pages
ISBN:9781450380072
DOI:10.1145/3395352
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 July 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. autoencoder
  2. deep learning
  3. mutual information estimation
  4. physical layer security
  5. secure encoding

Qualifiers

  • Short-paper

Funding Sources

Conference

WiSec '20
Sponsor:

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)32
  • Downloads (Last 6 weeks)3
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Coding Theory Advances in Physical‐Layer SecrecyPhysical‐Layer Security for 6G10.1002/9781394170944.ch2(19-42)Online publication date: 25-Oct-2024
  • (2023)Practical Wiretap Code Design by Concatenated Autoencoder and LDPC Codes2023 IEEE International Workshop on Information Forensics and Security (WIFS)10.1109/WIFS58808.2023.10375067(1-7)Online publication date: 4-Dec-2023
  • (2023)Benchmarking Neural Capacity Estimation: Viability and ReliabilityIEEE Transactions on Communications10.1109/TCOMM.2023.325525171:5(2654-2669)Online publication date: May-2023
  • (2023)Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance EvaluationIEEE Transactions on Communications10.1109/TCOMM.2023.323725971:3(1462-1474)Online publication date: Mar-2023
  • (2023)Secret Sharing Over a Gaussian Broadcast Channel: Optimal Coding Scheme Design and Deep Learning Approach at Short Blocklength2023 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT54713.2023.10206773(1961-1966)Online publication date: 25-Jun-2023
  • (2023)Secure Deep-JSCC Against Multiple EavesdroppersGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10436928(3433-3438)Online publication date: 4-Dec-2023
  • (2022)Deep Reinforcement Learning For Secure Communication2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)10.1109/VTC2022-Fall57202.2022.10013039(1-5)Online publication date: Sep-2022
  • (2022)Privacy-Aware Communication over a Wiretap Channel with Generative NetworksICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9747068(2989-2993)Online publication date: 23-May-2022
  • (2022)Security Gap Improvement of BICM Systems Through Bit-Labeling Optimization for the Gaussian Wiretap ChannelIEEE Access10.1109/ACCESS.2022.317248110(47805-47813)Online publication date: 2022
  • (2022)Secrecy Capacity-Approaching Neural Communications for Gaussian Wiretap ChannelArtificial Intelligence in China10.1007/978-981-16-9423-3_4(24-31)Online publication date: 22-Mar-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media