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Privacy-Protective Distributed Machine Learning Between Rich Devices and Edge Servers Using Confidence Level

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Big Data Intelligence and Computing (DataCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13864))

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Abstract

Many recent studies have focused on using the personal information collected by edge devices for machine learning on servers while protecting privacy. In most cases, a high-performance server aggregates and manages all data. However, users may refuse to pass on their personal information to an external server owing to the risk of information leakage. To address this concern, we propose a distributed machine learning model that does not extract any sensitive data from the edge device. In the proposed model, edge devices also run machine learning and integrates learning results obtained at edge servers with those obtained at edge devices by comparing their confidence levels. To validate the proposed model, we performed experiments on facial image recognition using Jetson Nano. Experimental results confirm that the proposed model enables the use of personal data for machine learning without transferring any sensitive information to the edge server. With this learning model, each user could use personal information securely and could receive the best match results.

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References

  1. Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., Sabella, D.: On multiaccess edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutorials 19(3), 1657–1681 (2017)

    Article  Google Scholar 

  2. Shokri, R., Stronati, M., Shmatikov, V.: Membership inference attacks against machine learning models. CoRR, Vol. abs/1610.05820 (2016)

    Google Scholar 

  3. Hong, S., Chandrasekaran, V., Kaya, Y., Dumitraş, T., Papernot, N.: On the effectiveness of mitigating data poisoning attacks with gradient shaping. CoRR, Vol. abs/2002.11497 (2020)

    Google Scholar 

  4. European Parliament and The Council of the European Union. The general data protection regulation (GDPR). https://eur-lex.europa.eu/eli/reg/2016/679/oj. Accessed 10 Apr 2021

  5. Murshed, M.S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., Hussain, F.: Machine learning at the network edge: a survey. ACM Comput. Surv. (CSUR) 54(8), 1–37 (2021)

    Article  Google Scholar 

  6. Shi, W., Cao, J., Zhang, Q., Li, Y., Lanyu, X.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  7. Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)

    Article  Google Scholar 

  8. Chen, N., Chen, Y., Song, S., Huang, C.T., Ye, X.: Poster abstract: smart urban surveillance using fog computing. In: 2016 IEEE/ACM Symposium on Edge Computing (SEC), pp. 95–96 (2016)

    Google Scholar 

  9. Tang, B., et al.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Ind. Inf. 13(5), 2140–2150 (2017)

    Article  Google Scholar 

  10. Lin, J., Yu, W., Yang, X., Yang, Q., Fu, X., Zhao, W.: A real-time En-route route guidance decision scheme for transportation-based cyberphysical systems. IEEE Trans. Veh. Technol. 66(3), 2551–2566 (2017)

    Article  Google Scholar 

  11. Lee, J., Kim, D., Park, J., Park, H.: A multi-server authentication protocol achieving privacy protection and traceability for 5G mobile edge computing. In: Proceedings of the 39th IEEE International Conference on Consumer Electronics (ICCE 2021) (2021)

    Google Scholar 

  12. Wang, S., et al.: A cloud-guided feature extraction approach for image retrieval in mobile edge computing. IEEE Trans. Mob. Comput. 20(2), 292–305 (2021)

    Article  Google Scholar 

  13. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)

    Article  Google Scholar 

  14. Li, L., Fan, Y., Tse, M., Lin, K.-Y.: A review of applications in federated learning. Comput. Ind. Eng. 149, 106854 (2020)

    Article  Google Scholar 

  15. Chen Zhang, Yu., Xie, H.B., Bin, Yu., Li, W., Gao, Y.: A survey on federated learning. Knowl. Based Syst. 216, 106775 (2021)

    Article  Google Scholar 

  16. Yang, T., et al.: Applied federated learning: improving google keyboard query suggestions. ArXiv, Vol. abs/1812.02903 (2018)

    Google Scholar 

  17. Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5(1), 1–19 (2021)

    Article  Google Scholar 

  18. Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 1–7 (2020)

    Article  Google Scholar 

  19. Ye, D., Rong, Yu., Pan, M., Han, Z.: Federated learning in vehicular edge computing: a selective model aggregation approach. IEEE Access 8, 23920–23935 (2020)

    Article  Google Scholar 

  20. Jiang, J.C., Kantarci, B., Oktug, S., Soyata, T.: Federated learning in smart city sensing: challenges and opportunities. Sensors 20(21), 6230 (2020)

    Article  Google Scholar 

  21. Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 739–753. IEEE (2019)

    Google Scholar 

  22. Song, M., et al.: Analyzing user-level privacy attack against federated learning. IEEE J. Sel. Areas Commun. 38(10), 2430–2444 (2020)

    Article  Google Scholar 

  23. Geiping, J., Bauermeister, H., Dröge, H., Moeller, M.: Inverting gradients-how easy is it to break privacy in federated learning? In: Advances in Neural Information Processing Systems, vol. 33, pp. 16937–16947 (2020)

    Google Scholar 

  24. Takano, S., Nakao, A., Yamaguchi, S., Oguchi, M.: Privacyprotective distributed machine learning using rich clients. In: 2021 International Conference on Emerging Technologies for Communications (ICETC 2021), IEICE Proceedings Series, vol. 68, no. C1–4 (2021)

    Google Scholar 

  25. Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  27. Tuysuzoglu, G., Birant, D.: Enhanced bagging (eBagging): a novel approach for ensemble learning. Int. Arab. J. Inf. Technol. 17(4), 515–528 (2020)

    Google Scholar 

  28. Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in ‘Real-Life’ Images: detection, alignment, and recognition (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Ochanomizu University, 2-1-1 Otsuka, Tokyo, Japan

    Saki Takano & Masato Oguchi

  2. The University of Tokyo, 7-3-1-1 Hongo, Bunkyo-ku, Tokyo, Japan

    Akihiro Nakao

  3. Kogakuin University, 1-24-2 Nishi-shinjuku, Suhinjuku-ku, Tokyo, Japan

    Saneyasu Yamaguchi

Authors
  1. Saki Takano
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  2. Akihiro Nakao
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  3. Saneyasu Yamaguchi
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  4. Masato Oguchi
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Corresponding author

Correspondence to Saki Takano .

Editor information

Editors and Affiliations

  1. Asia University, Taichung, Taiwan

    Ching-Hsien Hsu

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Mengwei Xu

  3. University of New Brunswick, Fredericton, NB, Canada

    Hung Cao

  4. Asia University, Taichung, Taiwan

    Hojjat Baghban

  5. University of Fiji, Samabula, Fiji

    A. B. M. Shawkat Ali

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Takano, S., Nakao, A., Yamaguchi, S., Oguchi, M. (2023). Privacy-Protective Distributed Machine Learning Between Rich Devices and Edge Servers Using Confidence Level. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_10

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  • DOI: https://doi.org/10.1007/978-981-99-2233-8_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2232-1

  • Online ISBN: 978-981-99-2233-8

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