Skip to main content
Springer Nature Link
Log in

Federated Learning in Side-Channel Analysis

  • Conference paper
  • First Online:
Information Security and Cryptology – ICISC 2020 (ICISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12593))

Included in the following conference series:

Abstract

Recently introduced federated learning is an attractive framework for the distributed training of deep learning models with thousands of participants. However, it can potentially be used with malicious intent. For example, adversaries can use their smartphones to jointly train a classifier for extracting secret keys from the smartphones’ SIM cards without sharing their side-channel measurements with each other. With federated learning, each participant might be able to create a strong model in the absence of sufficient training data. Furthermore, they preserve their anonymity. In this paper, we investigate this new attack vector in the context of side-channel attacks. We compare the federated learning, which aggregates model updates submitted by N participants, with two other aggregating approaches: (1) training on combined side-channel data from N devices, and (2) using an ensemble of N individually trained models. Our first experiments on 8-bit Atmel ATxmega128D4 microcontroller implementation of AES show that federated learning is capable of outperforming the other approaches.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Atchinson, B.K., Fox, D.M.: From the field: the politics of the health insurance portability and accountability act. Health Affairs 16(3), 146–150 (1997)

    Article  Google Scholar 

  2. Benadjila, R., Prouff, E., Strullu, R., Cagli, E., Dumas, C.: Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. ANSSI, France & CEA, LETI, MINATEC Campus, France, vol. 22 (2018). https://eprint.iacr.org/2018/053.pdf

  3. Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)

    Google Scholar 

  4. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  5. Brier, E., Clavier, C., Olivier, F.: Correlation power analysis with a leakage model. In: Joye, M., Quisquater, J.-J. (eds.) CHES 2004. LNCS, vol. 3156, pp. 16–29. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28632-5_2

    Chapter  Google Scholar 

  6. Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. In: Fischer, W., Homma, N. (eds.) CHES 2017. LNCS, vol. 10529, pp. 45–68. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66787-4_3

    Chapter  Google Scholar 

  7. CW308 UFO Target. https://wiki.newae.com/CW308_UFO_Target

  8. The Design of Rijndael. ISC. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-662-60769-5_9

  9. Das, D., Golder, A., Danial, J., Ghosh, S., Raychowdhury, A., Sen, S.: X-deepsca: cross-device deep learning side channel attack. In: Proceedings of the 56th Annual Design Automation Conference 2019, pp. 1–6 (2019)

    Google Scholar 

  10. Gilmore, R., Hanley, N., O’Neill, M.: Neural network based attack on a masked implementation of AES. In: 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 106–111. IEEE (2015)

    Google Scholar 

  11. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

  12. Jin, M., Zheng, M., Hu, H., Yu, N.: An enhanced convolutional neural network in side-channel attacks and its visualization. arXiv preprint arXiv:2009.08898 (2020)

  13. Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48405-1_25

    Chapter  Google Scholar 

  14. Kocher, P.C.: Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In: Koblitz, N. (ed.) CRYPTO 1996. LNCS, vol. 1109, pp. 104–113. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-68697-5_9

    Chapter  Google Scholar 

  15. Konečnỳ, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)

  16. Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)

  17. Kubota, T., Yoshida, K., Shiozaki, M., Fujino, T.: Deep learning side-channel attack against hardware implementations of AES. In: 2019 22nd Euromicro Conference on Digital System Design (DSD), pp. 261–268. IEEE (2019)

    Google Scholar 

  18. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., He, B.: A survey on federated learning systems: Vision, hype and reality for data privacy and protection (2019)

    Google Scholar 

  19. Maghrebi, H.: Deep learning based side channel attacks in practice. Technical Report, IACR Cryptology ePrint Archive 2019, vol. 578 (2019)

    Google Scholar 

  20. Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) SPACE 2016. LNCS, vol. 10076, pp. 3–26. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49445-6_1

    Chapter  Google Scholar 

  21. Martinasek, Z., Dzurenda, P., Malina, L.: Profiling power analysis attack based on MLP in DPA contest v4. 2. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP), pp. 223–226. IEEE (2016)

    Google Scholar 

  22. Martinasek, Z., Malina, L., Trasy, K.: Profiling power analysis attack based on multi-layer perceptron network. In: Mastorakis, N., Bulucea, A., Tsekouras, G. (eds.) Computational Problems in Science and Engineering. LNEE, vol. 343, pp. 317–339. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15765-8_18

    Chapter  Google Scholar 

  23. McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)

  24. NewAE Technology Inc.: Chipwhisperer. https://newae.com/tools/chipwhisperer

  25. O’Flynn, C., Chen, Z.D.: ChipWhisperer: an open-source platform for hardware embedded security research. In: Prouff, E. (ed.) COSADE 2014. LNCS, vol. 8622, pp. 243–260. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10175-0_17

    Chapter  Google Scholar 

  26. Perin, G., Ege, B., van Woudenberg, J.: Lowering the bar: deep learning for side-channel analysis (white-paper). In: Proceedings of BlackHat, pp. 1–15 (2018)

    Google Scholar 

  27. Polikar, R.: Ensemble learning. In: Ensemble Machine Learning, pp. 1–34. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4419-9326-7_1

  28. Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22, 400–407 (1951)

    Article  MathSciNet  Google Scholar 

  29. Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. In: Advances in Neural Information Processing Systems, pp. 4424–4434 (2017)

    Google Scholar 

  30. Timon, B.: Non-profiled deep learning-based side-channel attacks. IACR Cryptol. ePrint Arch. 2018, 196 (2018)

    Google Scholar 

  31. Voigt, P., von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). A Practical Guide. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7

    Book  Google Scholar 

  32. Wang, H.: Side-Channel Analysis of AES Based on Deep Learning. Master’s thesis, KTH, School of Electrical Engineering and Computer Science (EECS) (2019)

    Google Scholar 

  33. Wang, H., Brisfors, M., Forsmark, S., Dubrova, E.: How diversity affects deep-learning side-channel attacks. In: 2019 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC), pp. 1–7. IEEE (2019)

    Google Scholar 

  34. Wang, H., Dubrova, E.: Tandem deep learning side-channel attack against FPGA implementation of AES. Cryptology ePrint Archive, Report 2020/373 (2020). https://eprint.iacr.org/2020/373

  35. Wang, H., Forsmark, S., Brisfors, M., Dubrova, E.: Multi-source training deep learning side-channel attacks. IEEE 50th International Symposium on Multiple-Valued Logic (2020)

    Google Scholar 

  36. Wang, R., Wang, H., Dubrova, E.: Far field em side-channel attack on AES using deep learning. Cryptology ePrint Archive, Report 2020/1096 (2020). https://eprint.iacr.org/2020/1096

  37. Wu, Y., Shen, K., Chen, Z., Wu, J.: Automatic measurement of fetal cavum septum pellucidum from ultrasound images using deep attention network. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2511–2515. IEEE (2020)

    Google Scholar 

  38. Yang, G., Li, H., Ming, J., Zhou, Y.: CDAE: towards empowering denoising in side-channel analysis. In: International Conference on Information and Communications Security, pp. 269–286. Springer (2019)

    Google Scholar 

Download references

Acknowledgment

This work was supported in part by the research grant 2018-04482 from the Swedish Research Council.

Author information

Authors and Affiliations

  1. KTH Royal Institute of Technology, Stockholm, Sweden

    Huanyu Wang & Elena Dubrova

Authors
  1. Huanyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elena Dubrova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huanyu Wang .

Editor information

Editors and Affiliations

  1. Jeonbuk National University, Jeonju-si, Korea (Republic of)

    Deukjo Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, H., Dubrova, E. (2021). Federated Learning in Side-Channel Analysis. In: Hong, D. (eds) Information Security and Cryptology – ICISC 2020. ICISC 2020. Lecture Notes in Computer Science(), vol 12593. Springer, Cham. https://doi.org/10.1007/978-3-030-68890-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68890-5_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68889-9

  • Online ISBN: 978-3-030-68890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics