A trusted recommendation scheme for privacy protection based on federated learning

Abstract

With the convergence of the era of global news and the era of big data, the daily amount of news sent to the world is exploding. Users also face the problem of information overloads when they get massive information, which leads to how cloud servers push personalized data to users among massive data have become the focus of news companies. In order to obtain the push accuracy, the traditional recommendation system often makes deep mining of users’ privacy data, which makes users’ privacy cannot be guaranteed. In order to solve the above problems, this paper proposes a collaborative filtering algorithm recommendation system based on federated learning on end-edge-cloud. The exposure of data privacy was further prevented by adding Laplace noise to the training model through differential privacy technology. Finally, the training model and recommendation information is stored to the blockchain network to provide permanent storage, evidence chain and real-time traceability services.On the premise of protecting data privacy, this system provides cloud server with solutions to alleviate computing pressure, bandwidth pressure and improve news push accuracy through end-edge-cloud distributed learning.

This is a preview of subscription content, access via your institution.

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

References

  1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016)

  2. Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5, 1 (2017)

    Google Scholar 

  3. Ahmed, F., Shafiq, M.Z., Khakpour, A.R., Liu, A.X.: Optimizing internet transit routing for content delivery networks. IEEE/ACM Trans. Netw. 26, 1 (2017)

    Google Scholar 

  4. Al-Abbasi, A.O., Aggarwal, V., Ra, M.: Multi-tier caching analysis in CDN-based over-the-top video streaming systems. IEEE/ACM Trans. Netw. 27, 2 (2019)

    Article  Google Scholar 

  5. Ammad-ud-din, M., Ivannikova, E., Khan, S.A., Oyomno, W., Fu Q., Tan, K.E., Flanagan, A.: Federated collaborative filtering for privacy-preserving personalized recommendation system. Statistics (2019)

  6. Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., et al.: Hyperledger fabric: a distributed operating system for permissioned blockchains. In: 13th EuroSys Conference (EuroSys) (2018)

  7. Bagchi, S.: Performance and quality assessment of similarity measures in collaborative filtering using mahout. Procedia Comput. Sci. 50, 229–234 (2015)

    Article  Google Scholar 

  8. Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. Adv. Neural Inf. Process. Syst. 21, 289–296 (2008)

    Google Scholar 

  9. Chen, M., et al.: Analysis and scheduling in a 5G heterogeneous content delivery network. IEEE Access 2018, 6 (2018)

    Google Scholar 

  10. Chen, M., Wang, L., Chen, J., Wei, X., Lei, L.: A computing and content delivery network in the smart city: scenario, framework, and analysis. IEEE Netw. 33(2), 89–95 (2019). https://doi.org/10.1109/MNET.2019.1800253

    Article  Google Scholar 

  11. De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: cloud, IoT, Edge, and Fog. IEEE Access 2019, 7 (2019)

    Google Scholar 

  12. Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: a client level perspective. Statistics (2017)

  13. Huang, S., Jiang, X., Zhang, N., Zhang, C., Dang, D.: Collaborative filtering of web service based on MapReduce. In: Proceedings 2014 International Conference on Service Sciences (ICSS) (2014)

  14. Koskela, A., Honkela, A.: Learning rate adaptation for differentially private stochastic gradient descent. Statistics (2018)

  15. Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans. Ind. Inform. 16, 3 (2019)

    Google Scholar 

  16. Lundbaek, L.-N., D'Iddio, A.C., Huth, M.: Centrally governed blockchains: optimizing security, cost, and availability. In: Conference on Models, Algorithms, Logics and Tools in Honour of Kim G. Larsen on the Occasion of his 60th Birthday (2017)

  17. Ma, C., Li, J., Ding, M., et al.: On safeguarding privacy and security in the framework of federated learning. IEEE Netw. 99, 1–7 (2020)

    Google Scholar 

  18. McMahan, H.B., Moore, E., Ramage, D.: Communication-efficient learning of deep networks from decentralized data. In: 20th International Conference on Artificial Intelligence and Statistics (AISTATS) (2017)

  19. Min, X., Li, Q., Liu, L., Cui, L.: A Permissioned blockchain framework for supporting instant transaction and dynamic block size. Trustcom/bigdatase/ispa, IEEE (2017)

  20. Peng, X., Ren, J., She, L., Zhang, D., Li, J., Zhang, Y.: BOAT: a block-streaming app execution scheme for lightweight IoT devices. IEEE Internet Things J. 5(3), 1816–1829 (2018)

    Article  Google Scholar 

  21. Ren, J., Guo, H., Xu, C., Zhang, Y.: Serving at the edge: a scalable iot architecture based on transparent computing. IEEE Netw. 31(5), 96–105 (2017)

    Article  Google Scholar 

  22. Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. B 2010, 3–30 (2010)

    MathSciNet  MATH  Google Scholar 

  23. Sharma, P.K., Chen, M., Park, J.H.: a software defined fog node based distributed blockchain cloud architecture for IoT. IEEE Access 6, 115–124 (2018). https://doi.org/10.1109/ACCESS.2017.2757955

    Article  Google Scholar 

  24. Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2013)

  25. Sun, H., Yu, Y., Sha, K., Lou, B.: mVideo: edge computing based mobile video processing systems. IEEE Access 2019, 8 (2019)

    Google Scholar 

  26. Thomas, H., Ned, S.: Cloud-based commissioning of constrained devices using permissioned blockchains. In: IoTPTS '16:Proceedings of the 2nd ACM International Workshop on IoT Privacy, Trust, and Security (2016)

  27. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. 10(2), 12.1–12.19 (2019)

    Google Scholar 

  28. Yanxiang, L., Deke, G., Fei, C., Honghui, C.: User-based Clustering with Top-N Recommendation on Cold-Start Problem. In: 2013 third international conference on intelligent system design and engineering applications, Hong Kong (2013)

  29. Zhou, W., Li, R., Liu, W.: Collaborative filtering recommendation algorithm based on improved similarity. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (2020)

Download references

Acknowledgements

This research work is supposed by the National Key R&D Program of China (2018YFB1201500), National Natural Science Founds of China (62072368, 61773313, 61702411), National Natural Science Founds of Shaanxi (2017JQ6020, 2016JQ6041), Key Research and Development Program of Shaanxi Province (2020GY-039, 2017ZDXMGY-098, 2019TD-014)

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xinhong Hei.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 185 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Tian, Y., Yin, X. et al. A trusted recommendation scheme for privacy protection based on federated learning. CCF Trans. Netw. 3, 218–228 (2020). https://doi.org/10.1007/s42045-020-00045-8

Download citation

Keywords

  • Federated learning
  • Blockchain
  • Differential privacy
  • Recommendation system