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Differential private collaborative Web services QoS prediction

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Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. 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 model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user’s observed QoS values by applying differential privacy to the user’s QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user’s QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.

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  1. Berlioz, A., Friedman, A., Kaafar, M.A., Boreli, R., Berkovsky, S.: Applying differential privacy to matrix factorization. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 107–114. ACM (2015)

  2. Canny, J.: Collaborative filtering with privacy. In: 2002. Proceedings 2002 IEEE Symposium on Security and Privacy, pp. 45–57. IEEE

  3. Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized qos-aware Web service recommendation and visualization. IEEE Trans. Serv. Comput. 6(1), 35–47 (2013)

    Article  Google Scholar 

  4. Ding, Z., Yang, B., Gu̇ting, R.H., Li, Y.: Network-matched trajectory-based moving-object database: Models and applications. IEEE Trans. Intell. Transp. Syst. 16 (4), 1918–1928 (2015)

    Article  Google Scholar 

  5. Ding, Z., Yang, B., Chi, Y., Guo, L.: Enabling smart transportation systems: A parallel spatio-temporal database approach. IEEE Trans. Comput. 65(5), 1377–1391 (2016)

    Article  MathSciNet  Google Scholar 

  6. Dwork, C.: Differential privacy: a survey of results. In: International Conference on Theory and Applications of Models of Computation, pp. 1–19. Springer (2008)

  7. Dwork, C.: Differential privacy encyclopedia of cryptography and security (2011)

  8. Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference, pp. 265–284. Springer (2006)

  9. 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)

    Article  Google Scholar 

  10. Fletcher, K.K., Liu, X.F.: A collaborative filtering method for personalized preference-based service recommendation. In: IEEE International Conference on Web Services (ICWS), pp. 400–407. IEEE (2015)

  11. Gentry, C.: A fully homomorphic encryption scheme. PhD thesis Stanford University (2009)

  12. Guerraoui, R., Kermarrec, A.-M., Patra, R., Taziki, M.: D 2 p: distance-based differential privacy in recommenders. Proc. VLDB Endowment 8(8), 862–873 (2015)

    Article  Google Scholar 

  13. Guo, C., Jensen, C.S., Yang, B.: Towards total traffic awareness. SIGMOD Rec. 43(3), 18–23 (2014)

    Article  Google Scholar 

  14. Guo, C., Yang, B., Andersen, O., Jensen, C.S., Ecosky, K.T.: Reducing vehicular environmental impact through eco-routing. In: ICDE, pp. 1412–1415 (2015)

  15. Jorgensen, Z., Yu, T.: A privacy-preserving framework for personalized, social recommendations. In: EDBT, pp. 571–582 (2014)

  16. Kifer, D., Machanavajjhala, A.: No free lunch in data privacy. Inproceedings of the ACM SIGMOD International Conference on Management of data, pp. 193–204. ACM (2011)

  17. Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: 2007. ICDE 2007. IEEE 23rd International Conference on Data Engineering, pp. 106–115. IEEE (2007)

  18. Li, L., Liu, A., Li, Q., Liu, G., Li, Z.: Privacy-preserving collaborative Web services qos prediction via yao’s garbled circuits and homomorphic encryption. J. Web Eng. 15(3-4), 203–225 (2016)

    Google Scholar 

  19. Liu, A., Liu, H., Li, Q., Huang, L.-S., Xiao, M.-J.: Constraints-aware scheduling for transactional services composition. J. Comput. Sci. Technol. 24(4), 638–651 (2009)

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. Liu, A., Li, Q., Huang, L., Wen, S.: Shapley value based impression propagation for reputation management in Web service composition. In: 2012 IEEE 19th International Conference on Web Services, pp. 58–65. Honolulu (2012)

  22. 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)

    Article  Google Scholar 

  23. Liu, A., Li, Q., Zhou, X., Li, L., Liu, G., Gao, Y.: Rating propagation in Web services reputation systems: A fast shapley value approach. In: Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Bali, 2014. Proceedings, Part I, pp 466–480 (2014)

    Chapter  Google Scholar 

  24. Liu, A., Zheng, K., Li, L., Liu, G., Zhao, L., Zhou, X.: Efficient secure similarity computation on encrypted trajectory data. In: IEEE 31st International Conference on Data Engineering (ICDE), pp. 66–77. IEEE (2015)

  25. Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., system, R. Jurdak.: Bounded quadrant Error-bounded trajectory compression on the go. In: ICDE, pp. 987–998 (2015)

  26. Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., Lee, J., Jurdak, R.: A novel framework for online amnesic trajectory compression in resource-constrained environments. IEEE Trans. Knowl. Data Eng. 28(11), 2827–2841 (2016)

    Article  Google Scholar 

  27. Liu, X., Liu, A., Zhang, X., Li, Z., Liu, G., Zhao, L., Zhou, X.: When differential privacy meets randomized perturbation: A hybrid approach for privacy-preserving recommender system. In: International Conference on Database Systems for Advanced Applications, pp. 576–591. Springer (2017)

  28. Liu, A., Li, Z., Liu, G., Zheng, K., Zhang, M., Li, Q., Zhang, X.: Privacy-preserving task assignment in spatial crowdsourcing. J. Comput. Sci. Technol. 32(5), 905–918 (2017)

    Article  MathSciNet  Google Scholar 

  29. Liu, A., Wang, W., Shang, S., Li, Q., Zhang, X.: Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica (2017)

  30. Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations: accurate or private. Proc. VLDB Endowment 4(7), 440–450 (2011)

    Article  Google Scholar 

  31. McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pp. 19–30. ACM (2009)

  32. McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the net. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 627–636. ACM (2009)

  33. Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in neural information processing systems, pp. 1257–1264 (2008)

  34. Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D.: Privacy-preserving matrix factorization. In: Proceedings of the ACM SIGSAC conference on Computer & communications security, pp. 801–812. ACM (2013)

  35. Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: IEEE Symposium on Security and Privacy (SP), pp. 334–348. IEEE (2013)

  36. Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques. In: 2003. ICDM 2003. Third IEEE International Conference on Data Mining, pp. 625–628. IEEE (2003)

  37. Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 156–167. ACM (2012)

  38. Shang, S., Lu, H., Pedersen, T.B., Xie, X.: Finding traffic-aware fastest paths in spatial networks. In: SSTD, pp. 128–145 (2013)

    Chapter  Google Scholar 

  39. Shang, S., Lu, H., Pedersen, T.B., Xie, X.: Modeling of traffic-aware travel time in spatial networks. In: MDM, pp. 247–250 (2013)

  40. 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)

    Article  Google Scholar 

  41. Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T.B., Wen, J.: Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4), 723–746 (2015)

    Article  Google Scholar 

  42. Shang, S., Zheng, K., Jensen, C.S., Yang, B., Kalnis, P., Li, G., Wen, J.: Discovery of path nearby clusters in spatial networks. IEEE Trans. Knowl. Data Eng. 27(6), 1505–1518 (2015)

    Article  Google Scholar 

  43. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Wen, J.-R., Kalnis, P.: Collective travel planning in spatial networks. IEEE Trans. Knowl. Data Eng. 28(5), 1132–1146 (2016)

    Article  Google Scholar 

  44. Shang, S., Guo, D., Liu, J., Zheng, K., Wen, J.: Finding regions of interest using location based social media. Neurocomputing 173, 118–123 (2016)

    Article  Google Scholar 

  45. Shang, S., Chen, L., Jensen, C.S., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. IEEE Trans. Knowl. Data Eng. 29(7), 1549–1562 (2017)

    Article  Google Scholar 

  46. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)

    Google Scholar 

  47. Shen, Y., Jin, H.: Epicrec: Towards practical differentially private framework for personalized recommendation. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 180–191. ACM (2016)

  48. Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  49. Tang, M., Jiang, Y., Liu, J., Liu, X.: Location-aware collaborative filtering for qos-based service recommendation. In: IEEE 19th International Conference on Web Services (ICWS), pp. 202–209. IEEE (2012)

  50. Xie, K., Deng, K., Shang, S., Zhou, X., Zheng, K.: Finding alternative shortest paths in spatial networks. ACM Trans. Database Syst. 37(4), 29:1–29:31 (2012)

    Article  Google Scholar 

  51. Xie, Q., Shang, S., Yuan, B., Pang, C., Zhang, X.: Local correlation detection with linearity enhancement in streaming data. In: CIKM, pp. 309–318 (2013)

  52. Yang, B., Guo, C., Jensen, C.S., Kaul, M., Shang, S.: Stochastic skyline route planning under time-varying uncertainty. In: ICDE, pp. 136–147 (2014)

  53. Yang, B., Dai, J., Guo, C., Jensen, C.S.: Pace: A PAth-CEntric paradigm for stochastic path finding. VLDB Journal online first (2017)

  54. Yao, L., Sheng, Q.Z., Segev, A., Yu, J.: Recommending Web services via combining collaborative filtering with content-based features. In: 2013 IEEE 20th International Conference on Web Services (ICWS), pp. 42–49. IEEE (2013)

  55. Yu, Q., Zheng, Z., Wang, H.: Trace norm regularized matrix factorization for service recommendation. In: IEEE 20th International Conference on Web Services (ICWS), pp. 34–41. IEEE (2013)

  56. Yu, D., Liu, Y., Xu, Y., Yin, Y.: Personalized qos prediction for Web services using latent factor models. In: IEEE International Conference on Services Computing (SCC), pp. 107–114. IEEE (2014)

  57. Zhang, S., Ford, J., Makedon, F.: Deriving private information from randomly perturbed ratings. In: Proceedings of the SIAM International Conference on Data Mining, pp. 59–69. SIAM (2006)

  58. Zhang, Q., Ding, C.: Collaborative filtering based service ranking using invocation histories. In: 2011 IEEE International Conference on Web Services (ICWS). IEEE, pp 195–202 (2011)

  59. Zheng, Z., Ma, H., Lyu, M.R., King, I.: Wsrec: a collaborative filtering based Web service recommender system. In: 2009. ICWS 2009. IEEE International Conference on Web Services, pp. 437–444. IEEE (2009)

  60. Zheng, Z., Zhang, Y., Lyu, M.R.: Distributed qos evaluation for real-world Web services. In: 2010 IEEE International Conference on Web Services (ICWS), pp. 83–90. IEEE (2010)

  61. 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 (2011)

    Article  Google Scholar 

  62. Zheng, K., Shang, S., Yuan, N.J., Yang, Y.: Towards efficient search for activity trajectories. In: ICDE, pp. 230–241 (2013)

  63. Zheng, K., Zheng, Y., Yuan, N.J., Shang, S.: On discovery of gathering patterns from trajectories. In: ICDE, pp. 242–253 (2013)

  64. Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative Web service qos prediction via neighborhood integrated matrix factorization. IEEE Trans. Serv. Comput. 6(3), 289–299 (2013)

    Article  Google Scholar 

  65. Zheng, K., Zheng, Y., Yuan, N.J., Shang, S., Zhou, X.: Online discovery of gathering patterns over trajectories, vol. 26 (2014)

    Article  Google Scholar 

  66. Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive top-k spatial keyword queries. In: ICDE, pp. 423–434 (2015)

  67. 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 (ICWS), pp. 241–248. IEEE (2015)

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Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313)

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Correspondence to Shuo Shang.

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This article belongs to the Topical Collection: Special Issue on Web and Big Data

Guest Editors: Junjie Yao, Bin Cui, Christian S. Jensen, and Zhe Zhao

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Liu, A., Shen, X., Li, Z. et al. Differential private collaborative Web services QoS prediction. World Wide Web 22, 2697–2720 (2019).

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