Differential Privacy Enabled Deep Neural Networks for Wireless Resource Management | Mobile Networks and Applications Skip to main content

Advertisement

Springer Nature Link
Log in

Differential Privacy Enabled Deep Neural Networks for Wireless Resource Management

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Deep neural networks (DNN) are increasingly utilized for wireless resource allocations in beyond 5G/6G networks to solve the high computational time problem of iterative algorithms. The main issue of neural network-based wireless resource allocation schemes is that it is possible to regain sensitive details about the training data from model parameters. However, existing works do not consider the privacy leakage issues of the neural networks while allocating wireless resources. To resolve this problem, we develop a framework using two DNN architectures, e.g., multi-layer perceptron (MLP) network and convolutional neural network (CNN) based on the concept of differential privacy (DP) which is usually implemented for data privacy protection based on neural networks incorporating appropriately calibrated noise to reduce the sensitivity of the gradients. The results of the numerical simulation indicate that the DP-enabled CNN performs better achievable rate compared to DP-enabled MLP. Yet, the proposed framework solves the high computational time problem of the iterative algorithm, i.e., stochastic weighted minimum mean square error (SWMMSE). Evaluation illustrates that our proposed framework facilitates the design of privacy-enabled resource management in different sized wireless networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The codes used to generate the data for the current study are available from the corresponding author upon reasonable request.

Notes

  1. https://colab.research.google.com

References

  1. Teng Y, Liu M, Yu FR, Leung VC, Song M, Zhang Y (2018) Resource allocation for ultra-dense networks: a survey, some research issues and challenges. IEEE Commun Surveys Tutor 21(3):2134–2168

    Article  Google Scholar 

  2. ElHalawany BM, Hashad O, Wu K, Tag Eldien AS (2020) Uplink resource allocation for multi-cluster internet-of-things deployment underlaying cellular networks. Mob Netw Appl 25(1):300–313

    Article  Google Scholar 

  3. Luo Z-Q, Zhang S (2008) Dynamic spectrum management: Complexity and duality. IEEE J Select Topics Signal Process 2(1):57–73

    Article  Google Scholar 

  4. Liu D, Cui H, Wu J, Luo C (2016) Resource allocation for uncoded multi-user video transmission over wireless networks. Mob Netw Appl 21(6):950–961

    Article  Google Scholar 

  5. Zhang X, Yang K, Wang P, Hong X (2015) Energy efficient bandwidth allocation in heterogeneous wireless networks. Mob Netw Appl 20(2):137–146

    Article  Google Scholar 

  6. Mowla MM, Ahmad I, Habibi D, Phung QV (2017) A green communication model for 5G systems. IEEE Trans Green Commun Netw 1(3):264–280

    Article  Google Scholar 

  7. Huang Y, Xu H, Gao H, Ma X, Hussain W (2021) SSUR: An approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center. IEEE Trans Green Commun Netw 5(2):670–681

    Article  Google Scholar 

  8. Zhu Y, Zhang W, Chen Y, Gao H (2019) A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment. EURASIP J Wirel Commun Netw 2019(1):1–18

    Article  Google Scholar 

  9. Sarker IH, Khan AI, Abushark YB, Alsolami F (2022) Internet of things (IoT) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mob Netw Appl 1–17

  10. Fourati H, Maaloul R, Chaari L (2021) A survey of 5G network systems: challenges and machine learning approaches. Int J Machine Learn Cybern 12(2):385–431

    Article  Google Scholar 

  11. Gao H, Liu C, Yin Y, Xu Y, Li Y (2021) A hybrid approach to trust node assessment and management for VANETs cooperative data communication: historical interaction perspective. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2021.3129458

  12. Rahman MH, Mowla MM (2020) A deep neural network based optimization approach for wireless resource management, 803–806. https://doi.org/10.1109/TENSYMP50017.2020.9230822

  13. Lee W (2018) Resource allocation for multi-channel underlay cognitive radio network based on deep neural network. IEEE Commun Lett 22(9):1942–1945

    Article  Google Scholar 

  14. Ku M-L, Lin T-J (2021) Neural-network-based power control prediction for solar-powered energy harvesting communications. IEEE Int Things J 8(16):12983–12998. https://doi.org/10.1109/JIOT.2021.3064150

    Article  Google Scholar 

  15. Sun H, Chen X, Shi Q, Hong M, Fu X, Sidiropoulos ND (2018) Learning to optimize: Training deep neural networks for interference management. IEEE Trans Signal Process 66(20):5438–5453

    Article  MathSciNet  Google Scholar 

  16. Rahman MH, Mowla MM, Shanto S (2020) Convolutional neural network based optimization approach for wireless resource management, 280–285. https://doi.org/10.1109/ICAICT51780.2020.9333532

  17. Shi Q, Razaviyayn M, Luo Z-Q, He C (2011) An iteratively weighted mmse approach to distributed sum-utility maximization for a mimo interfering broadcast channel. IEEE Trans Signal Process 59 (9):4331–4340

    Article  MathSciNet  Google Scholar 

  18. Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models. IEEE, New Jersey, pp 3–18

    Google Scholar 

  19. Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. 308–318

  20. Chaudhuri K, Monteleoni C, Sarwate AD (2011) Differentially private empirical risk minimization. Journal of Machine Learning Research 12(3)

  21. Shokri R, Shmatikov V (2015) Privacy-preserving deep learning. 1310–1321

  22. Gao H, Qiu B, Barroso RJD, Hussain W, Xu Y, Wang X (2022) TSMAE: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2022.3163144

  23. Lei L, You L, Dai G, Vu TX, Yuan D, Chatzinotas S (2017) A deep learning approach for optimizing content delivering in cache-enabled hetnet. IEEE, New York, pp 449–453

    Google Scholar 

  24. Ahmed KI, Tabassum H, Hossain E (2019) Deep learning for radio resource allocation in multi-cell networks. IEEE Netw 33(6):188–195

    Article  Google Scholar 

  25. Fredrikson M, Jha S, Ristenpart T (2015) Model inversion attacks that exploit confidence information and basic countermeasures, 1322–1333

  26. Cortés J, Dullerud GE, Han S, Le Ny J, Mitra S, Pappas GJ (2016) Differential privacy in control and network systems. In: 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, pp 4252–4272

  27. Zhang J, Zhao Y, Wang J, Chen B (2020) Fedmec: improving efficiency of differentially private federated learning via mobile edge computing. Mob Netw Appl 25(6):2421–2433

    Article  Google Scholar 

  28. Xu X, Liu X, Xu Z, Wang C, Wan S, Yang X (2020) Joint optimization of resource utilization and load balance with privacy preservation for edge services in 5G networks. Mob Netw Appl 25(2):713–724

    Article  Google Scholar 

  29. Wang S, Li J, Wu G, Chen H, Sun S (2022) Joint optimization of task offloading and resource allocation based on differential privacy in vehicular edge computing. IEEE Transactions on Computational Social Systems 9(1):109–119. https://doi.org/10.1109/TCSS.2021.3074949

    Article  Google Scholar 

  30. Baligh H, Hong M, Liao W-C, Luo Z-Q, Razaviyayn M, Sanjabi M, Sun R (2014) Cross-layer provision of future cellular networks: a wmmse-based approach. IEEE Signal Proc Mag 31(6):56–68

    Article  Google Scholar 

  31. Verdu S, et al. (1998) Multiuser detection, 57–73

  32. Fanti G, Pihur V, Erlingsson Ú (2016) Building a rappor with the unknown: Privacy-preserving learning of associations and data dictionaries. Proceedings on Privacy Enhancing Technologies 2016(3):41–61

    Article  Google Scholar 

  33. Ning B, Sun Y, Tao X, Li G (2021) Differential privacy protection on weighted graph in wireless networks. Ad Hoc Netw 110:102303. https://doi.org/10.1016/j.adhoc.2020.102303

    Article  Google Scholar 

  34. Zhang Y, Pan J, Qi L, He Q (2021) Privacy-preserving quality prediction for edge-based IoT services. Futur Gener Comput Syst 114:336–348. https://doi.org/10.1016/j.future.2020.08.014

    Article  Google Scholar 

  35. Bagdasaryan E, Poursaeed O, Shmatikov V (2019) Differential privacy has disparate impact on model accuracy. Adv Neural Inf Process Syst 32:15479–15488

    Google Scholar 

  36. Nicolas P, Shuang S, Ilya M, Ananth R, Kunal T, Úlfar E (2018) Scalable private learning with pate. In: 6th International Conference on Learning Representations, ICLR. https://doi.org/10.48550/arXiv.1802.08908

Download references

Acknowledgements

The authors did not receive support from any organization for this research work.

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology (RUET), Kazla, Rajshahi, 6204, Bangladesh

    Md Habibur Rahman, Md Munjure Mowla & Shahriar Shanto

Authors
  1. Md Habibur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md Munjure Mowla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahriar Shanto
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Md Habibur Rahman conceived the study, analyzed the data, and wrote the manuscript. Md Munjure Mowla supervised this work. Shahriar Shanto analyzed the data and prepared figures. All authors edited the manuscript and approved the final draft.

Corresponding author

Correspondence to Md Habibur Rahman.

Ethics declarations

Conflict of Interests

The authors declare no competing interests that are relevant to the contents of this article.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahman, M.H., Mowla, M.M. & Shanto, S. Differential Privacy Enabled Deep Neural Networks for Wireless Resource Management. Mobile Netw Appl 27, 2153–2162 (2022). https://doi.org/10.1007/s11036-022-02013-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-022-02013-6

Keywords

Search

Navigation