Advertisement

Privacy-Preserving Collaborative Web Services QoS Prediction via Differential Privacy

  • Shushu Liu
  • An LiuEmail author
  • Zhixu Li
  • Guanfeng Liu
  • Jiajie Xu
  • Lei Zhao
  • Kai Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10366)

Abstract

Collaborative Web services QoS prediction has become an important tool for the generation of accurate personalized QoS. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy preserving technique, into the preprocess of QoS data prediction. We implement the proposed approach based on a general approach named Laplace mechanism and conduct extensive experiments to study its performance on a real world dataset. The experiments evaluate the privacy-accuracy trade-off on different settings and show that under some constraint, our proposed approach can achieve a better performance than baselines.

Keywords

Collaborative QoS prediction Privacy-preserving Differential privacy Data distribution 

Notes

Acknowledgment

Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313) and Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223.

References

  1. 1.
    An, L., Liu, H., Li, Q., Huang, L., Xiao, M.: Constraints-aware scheduling for transactional services composition. J. Comput. Sci. Technol. 24(4), 638–651 (2009)CrossRefGoogle Scholar
  2. 2.
    Berlioz, A., Friedman, A., Kaafar, M.A., Boreli, R., Berkovsky, S.: Applying differential privacy to matrix factorization. In: The ACM Conference, pp. 107–114 (2015)Google Scholar
  3. 3.
    Canny, J.: Collaborative filtering with privacy via factor analysis. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 238–245 (2002)Google Scholar
  4. 4.
    Dimitrakakis, C., Nelson, B., Mitrokotsa, A., Rubinstein, B.I.P.: Robust and private Bayesian inference. Arxiv 8776, 291–305 (2014)Google Scholar
  5. 5.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). doi: 10.1007/11787006_1 CrossRefGoogle Scholar
  6. 6.
    Dwork, C., Mcsherry, F., Nissim, K.: Calibrating noise to sensitivity in private data analysis. In: VLDB Endowment (2014)Google Scholar
  7. 7.
    Erkin, Z., Veugen, T., Toft, T., Lagendijk, R.L.: Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Trans. Inf. Forensics Secur. 7(3), 1053–1066 (2012)CrossRefGoogle Scholar
  8. 8.
    Gentry, C.: A fully homomorphic encryption scheme (2009)Google Scholar
  9. 9.
    Liu, A., Li, Q., Huang, L., Xiao, M.: FACTS: a framework for fault-tolerant composition of transactional web services. IEEE Trans. Serv. Comput. 3(1), 46–59 (2010)CrossRefGoogle Scholar
  10. 10.
    Liu, A., Li, Q., Huang, L., Ying, S., Xiao, M.: Coalitional game for community-based autonomous web services cooperation. IEEE Trans. Serv. Comput. 6(3), 387–399 (2013)CrossRefGoogle Scholar
  11. 11.
    Liu, A., Zhengy, K., Liz, L., Liu, G., Zhao, L., Zhou, X.: Efficient secure similarity computation on encrypted trajectory data. In: IEEE International Conference on Data Engineering, pp. 66–77 (2015)Google Scholar
  12. 12.
    Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations: accurate or private. In: VLDB Endowment (2011)Google Scholar
  13. 13.
    Mcsherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the net. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 627–636 (2009)Google Scholar
  14. 14.
    Mcsherry, F., Talwar, K.: Mechanism design via differential privacy. In: IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 94–103 (2007)Google Scholar
  15. 15.
    Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques. In: Proceedings of 3rd IEEE International Conference on Data Mining (ICDM 2003), 19–22 December 2003, Melbourne, Florida, USA, pp. 625–628 (2003)Google Scholar
  16. 16.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: International Conference on Neural Information Processing Systems, pp. 1257–1264 (2007)Google Scholar
  17. 17.
    Shang, S., Chen, L., Wei, Z., Jensen, C., Wen, J.R., Kalnis, P.: Collective travel planning in spatial networks. IEEE Trans. Knowl. Data Eng. 28(5), 1132–1146 (2016)CrossRefGoogle Scholar
  18. 18.
    Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449–468 (2014)CrossRefGoogle Scholar
  19. 19.
    Tang, M., Jiang, Y., Liu, J., Liu, X.: Location-aware collaborative filtering for QoS-based service recommendation. In: IEEE International Conference on Web Services, pp. 202–209 (2012)Google Scholar
  20. 20.
    Yanga, L.I., Wen, W., Xie, G.Q.: Survey of research on differential privacy. Appl. Res. Comput. 29(9), 3201–3582 (2012)Google Scholar
  21. 21.
    Yu, Q., Zheng, Z., Wang, H.: Trace norm regularized matrix factorization for service recommendation. In: IEEE International Conference on Web Services, pp. 34–41 (2013)Google Scholar
  22. 22.
    Zhang, Q., Ding, C., Chi, C.H.: Collaborative filtering based service ranking using invocation histories. In: IEEE International Conference on Web Services, pp. 195–202 (2011)Google Scholar
  23. 23.
    Zhang, S., Ford, J., Makedon, F.: Deriving private information from randomly perturbed ratings. In: SIAM International Conference on Data Mining, 20–22 April 2006, Bethesda, MD, USA (2006)Google Scholar
  24. 24.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: a collaborative filtering based web service recommender system. In: IEEE International Conference on Web Services, pp. 437–444 (2009)Google Scholar
  25. 25.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2010)CrossRefGoogle Scholar
  26. 26.
    Zheng, Z., Zhang, Y., Lyu, M.R.: Distributed QoS evaluation for real-world web services. In: IEEE International Conference on Web Services, pp. 83–90 (2010)Google Scholar
  27. 27.
    Zheng, Z., Zhang, Y., Lyu, M.R.: Investigating QoS of real-world web services. IEEE Trans. Serv. Comput. 7(1), 32–39 (2014)CrossRefGoogle Scholar
  28. 28.
    Zhu, J., He, P., Zheng, Z., Lyu, M.R.: A privacy-preserving QoS prediction framework for web service recommendation. In: IEEE International Conference on Web Services, pp. 241–248 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shushu Liu
    • 1
  • An Liu
    • 1
    Email author
  • Zhixu Li
    • 1
  • Guanfeng Liu
    • 1
  • Jiajie Xu
    • 1
  • Lei Zhao
    • 1
  • Kai Zheng
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina

Personalised recommendations