Abstract
With a growing number of alternative Web services that provide the same functionality, QoS-aware Web service recommendation is becoming increasingly important. However, collecting users’ observed QoS values is a challenging task for a recommender system. First, users don’t want to supply their observed QoS values due to privacy. Second, some user-contributed QoS values may be untrustworthy. There have been some centralized works on credible QoS prediction or privacy-preserving QoS Prediction. However, no research has been done to solve both the two problems simultaneously. Also, it’s difficult to guarantee the fairness and independence of the central server. In this paper, we propose a Blockchain-based Credible and Privacy-Preserving QoS-Aware Web service Recommendation framework. We first separate the traditional Matrix Factorization model into two disjoint parts: private factors and public factors, and train public factors collaboratively while keeping private factors secret. Then, we use blockchain, which based on the peer-to-peer network, to implement our proposed model. Through blockchain, users who don’t trust each other can reach a consensus without a central server. We conduct a series of experiments on a realworld dataset and analyze the proposed scheme in terms of accuracy, privacy, security, and complexity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McIlraith, S.A., Son, T.C., Zeng, H.: Semantic web services. IEEE Intell. Syst. 16(2), 46–53 (2001)
Duan, Q., Yan, Y., Vasilakos, A.V.: A survey on service-oriented network virtualization toward convergence of networking and cloud computing. IEEE Trans. Netw. Serv. Manag. 9(4), 373–392 (2012)
Zeng, L., Benatallah, B., Ngu, A.H., Dumas, M., Kalagnanam, J., Chang, H.: QoS-aware middleware for web services composition. IEEE Trans. Softw. Eng. 30(5), 311–327 (2004)
Konecný, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated optimization: distributed machine learning for on-device intelligence. CoRR 161002527(5), 1–38 (2016)
Castro, M., Liskov, B.: Practical Byzantine fault tolerance and proactive recovery. ACM Trans. Comput. Syst. (TOCS) 20(4), 398–461 (2018)
Blanchard, P., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. In: Annual Conference on Neural Information Processing Systems 2017, pp. 119–129 (2017)
Pedersen, T.P.: Non-interactive and information-theoretic secure verifiable secret sharing. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 129–140. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-46766-1_9
Zheng, Z., Zhang, Y., Lyu, M.R.: Distributed QoS evaluation for real-world web services. In: ICWS 2010 Proceedings of the 2010 IEEE International Conference on Web Services, pp. 83–90. IEEE (2010)
Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. In: 14th Proceedings of Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann (1998)
Sarwar, B.M., Karypis, G., Konstan, J.A., et al.: Item-based collaborative filtering recommendation algorithms. In 10th Proceedings of the International Conference on World Wide Web, pp. 285–295. ACM (2001)
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)
Qiu, W., Zheng, Z., Wang, X., Yang, X., Lyu, M.R.: Reputation-aware QoS value prediction of web services. In: SCC 2013 Proceedings of the 2013 IEEE International Conference on Services Computing, pp. 41–48. IEEE (2013)
Xu, J., Zheng, Z., Lyu, M.R.: Web service personalized quality of service prediction via reputation-based matrix factorization. IEEE Trans. Reliab. 65(1), 28–37 (2016)
Chen, L., Feng, Y., Wu, J.: Collaborative QoS prediction via feedback-based trust model. In: Proceedings of the 6th IEEE International Conference on Service-Oriented Computing and Applications (SOCA), pp. 206–213. IEEE (2013)
Wu, C., Qiu, W., Zheng, Z., Wang, X., Yang, X.: Qos prediction of web services based on two-phase k-means clustering. In: Proceedings of the 2015 IEEE International Conference on Web Services (ICWS), pp. 161–168. IEEE (2015)
Zhu, J., He, P., Zheng, Z., Lyu, M.R.: A privacy-preserving QoS prediction framework for web service recommendation. In: Proceedings of the 2015 IEEE International Conference on Web Services (ICWS), pp. 241–248. IEEE (2015)
Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: Random-data perturbation techniques and privacy-preserving data mining. Knowl. Inf. Syst. 7(4), 387–414 (2005)
Badsha, S., Yi, X., Khalil, I., Liu, D., Nepal, S., Bertino, E.: Privacy preserving location recommendations. In: Bouguettaya, A., et al. (eds.) WISE 2017. LNCS, vol. 10570, pp. 502–516. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68786-5_40
Liu, X., et al.: When differential privacy meets randomized perturbation: a hybrid approach for privacy-preserving recommender system. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 576–591. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_36
Polat, H., Du, W.: Privacy-preserving top-n recommendation on horizontally partitioned data. In: The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), pp. 725–731. IEEE (2005)
Zhan, J., Hsieh, C.-L., Wang, I.-C., Hsu, T.-S., Liau, C.-J., Wang, D.-W.: Privacy-preserving collaborative recommender systems. IEEE Trans. Syst. Man Cybern. 40(4), 472–476 (2010)
Canny, J.: Collaborative filtering with privacy. In: Proceedings 2002 IEEE Symposium on Security and Privacy, pp. 45–57. IEEE (2002)
Acknowledgments
The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China (11801595, 61722214), the Natural Science Foundation of Guangdong (2018A030310076), the Guangdong Basic and Applied Basic Research Foundation (2019A1515011043) and the CCF-Tencent Open Fund WeBank Special Funding.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, X., Du, E., Chen, C., Zheng, Z., Cai, T., Yan, Q. (2020). Blockchain-Based Credible and Privacy-Preserving QoS-Aware Web Service Recommendation. In: Zheng, Z., Dai, HN., Tang, M., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore. https://doi.org/10.1007/978-981-15-2777-7_51
Download citation
DOI: https://doi.org/10.1007/978-981-15-2777-7_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2776-0
Online ISBN: 978-981-15-2777-7
eBook Packages: Computer ScienceComputer Science (R0)