Top

Journal of Intelligent Information Systems

Published in:

20-08-2018

Privacy-preserving shared collaborative web services QoS prediction

Authors: An Liu, Xindi Shen, Haoran Xie, Zhixu Li, Guanfeng Liu, Jiajie Xu, Lei Zhao, Fu Lee Wang

Published in: Journal of Intelligent Information Systems | Issue 1/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Collaborative Web services QoS prediction (CQoSP) has been proved to be an effective tool to predict unknown QoS values of services. Recently a number of efforts have been made in this area, focusing on improving the accuracy of prediction. In this paper, we consider a novel kind of CQoSP, shared CQoSP, where multiple parties share their data with each other in order to provide more accurate prediction than a single party could do. To encourage data sharing, we propose a privacy-preserving framework which enables shared collaborative QoS prediction without leaking the private information of the involved party. Our framework is based on differential privacy, a rigorous and provable privacy model. We conduct extensive experiments on a real Web services QoS dataset. Experimental results show the proposed framework increases prediction accuracy while ensuring the privacy of data owners.

previous article A knowledge-based multi-criteria collaborative filtering approach for discovering services in mobile cloud computing platforms

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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

Calandrino, J.A., Kilzer, A., Narayanan, A. , Felten, E.W., Shmatikov, V. (2011). “you might also like:” privacy risks of collaborative filtering. In 2011 IEEE Symposium on security and privacy (SP) (pp. 231–246). IEEE.

Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H. (2013). Personalized QoS-aware web service recommendation and visualization. IEEE Transactions on Services Computing, 6(1), 35–47.CrossRef

Dwork, C. (2008). Differential privacy: a survey of results. In International conference on theory and applications of models of computation (pp. 1–19). Springer.

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

Erkin, Z., Veugen, T., Toft, T., Lagendijk, R.L. (2012). Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Transactions on Information Forensics and Security, 7(3), 1053–1066.CrossRef

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

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

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

Kifer, D., & Machanavajjhala, A. (2011). No free lunch in data privacy. In Proceedings of the 2011 ACM SIGMOD international conference on management of data (pp. 193–204). ACM.

Li, N., Li, T., Venkatasubramanian, S. (2007). t-closeness: privacy beyond k-anonymity and l-diversity In IEEE 23rd International conference on data engineering, 2007. ICDE 2007 (pp. 106–115). IEEE.

Li, L., An, L., Li, Q., Liu, G., Li, Z. (2016). Privacy-preserving collaborative web services QoS prediction via Yao’s garbled circuits and homomorphic encryption. Journal of Web Engineering, 15(3-4), 203– 225.

Liu, A., Liu, H., Li, Q., Huang, L., Xiao, M. (2009). Constraints-aware scheduling for transactional services composition. Journal of Computer Science and Technology, 24(4), 638–651.CrossRef

Liu, A., Li, Q., Huang, L., Mingjun, X. (2010). FACTS A framework for fault-tolerant composition of transactional web services. IEEE Transactions of Services Computing, 3(1), 46–59.CrossRef

Liu, A., Li, Q., Huang, L., Ying, S., Xiao, M. (2013). Coalitional game for community-based autonomous web services cooperation. IEEE Transactions of Services Computing, 6(3), 387–399.CrossRef

Liu, A., Zhengy, K., Lu, L., Liu, G., Zhao, L., Zhou, X. (2015a). Efficient secure similarity computation on encrypted trajectory data. In 2015 IEEE 31st International conference on data engineering (ICDE) (pp. 66–77). IEEE.

Liu, S., Liu, A., Liu, G., Li, Z., Xu, J., Zhao, P., Zhao, L. (2015b). A secure and efficient framework for privacy preserving social recommendation. In Web Technologies and Applications - 17th Asia-PacificWeb Conference, APWeb 2015, Guangzhou, China, September 18-20, 2015, Proceedings (pp. 781–792).CrossRef

Liu, A., Li, Z., Liu, G., Zheng, K., Zhang, M., Li, Q., Zhang, X. (2017a). Privacy-preserving task assignment in spatial crowdsourcing. Journal of Computer Science and Technology, 32(5), 905–918.MathSciNetCrossRef

Liu, S., An, L., Li, Z., Liu, G., Xu, J., Zhao, L., Zheng, K. (2017b). Privacy-preserving collaborative web services QoS prediction via differential privacy. In Asia-Pacific Web (APWeb) and web-age information management (WAIM) joint conference on web and big data (pp. 200–214). Springer.

Liu, X., Liu, A., Zhang, X., Li, Z., Liu, G., Zhao, L., Zhou, X. (2017c). 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.

Liu, A., Wang, W., Li, Z., Liu, G., Li, Q., Zhou, X., Zhang, X. (2018a). A privacy-preserving framework for trust-oriented point-of-interest recommendation. IEEE Access, 6, 393–404.CrossRef

Liu, A., Wang, W., Shang, S., Li, Q., Zhang, X. (2018b). Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica, 22(2), 335–362.CrossRef

McSherry, F.D. (2009). 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.

McSherry, F., & Mironov, I. (2009). 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.

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

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

Polat, H., & Du, W. (2003). Privacy-preserving collaborative filtering using randomized perturbation techniques. In Third IEEE International conference on data mining, 2003. ICDM 2003 (pp. 625–628). IEEE.

Qi, L., Xiang, H., Dou, W., Yang, C., Qin, Y., Zhang, X. (2017). Privacy-preserving distributed service recommendation based on locality-sensitive hashing. In 2017 IEEE International conference on web services (ICWS) (pp. 49–56). IEEE.

Shen, Y., & Jin, H. (2016). 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.

Shokri, R., Pedarsani, P., Theodorakopoulos, G., Hubaux, J.-P. (2009). Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. In Proceedings of the third ACM conference on recommender systems (pp. 157–164). ACM.

Sweeney, L. (2002). k-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557–570.MathSciNetCrossRef

Tang, M., Jiang, Y., Liu, J., Liu, X. (2012). Location-aware collaborative filtering for QoS-based service recommendation. In 2012 IEEE 19th International conference on web services (ICWS) (pp. 202–209). IEEE.

Yao, L., Sheng, Q.Z., Segev, A., Yu, J. (2013). 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.

Zhang, S., Ford, J., Makedon, F. (2006). Deriving private information from randomly perturbed ratings. In Proceedings of the 2006 SIAM international conference on data mining (pp. 59–69). SIAM.

Zhang, Q., Ding, C., Chi, C.-H. (2011). Collaborative filtering based service ranking using invocation histories. In 2011 IEEE International conference on web services (ICWS) (pp. 195–202). IEEE.

Zheng, Z., Ma, H., Lyu, M.R., King, I. (2009). Wsrec: A collaborative filtering based web service recommender system. In IEEE International conference on web services, 2009. ICWS 2009 (pp. 437–444). IEEE.

Zheng, Z., Zhang, Y., Lyu, M.R. (2010). Distributed QoS evaluation for real-world web services. In 2010 IEEE International conference on web services (ICWS) (pp. 83–90). IEEE.

Zheng, Z., Zhang, Y., Lyu, M.R. (2014). Investigating QoS of real-world web services. IEEE Transactions on Services Computing, 7(1), 32–39.CrossRef

Zhu, J., He, P., Zheng, Z., Lyu, M.R. (2015). A privacy-preserving QoS prediction framework for web service recommendation. In 2015 IEEE International conference on web services (ICWS) (pp. 241–248). IEEE.

Title: Privacy-preserving shared collaborative web services QoS prediction
Authors: An Liu
Xindi Shen
Haoran Xie
Zhixu Li
Guanfeng Liu
Jiajie Xu
Lei Zhao
Fu Lee Wang
Publication date: 20-08-2018
Publisher: Springer US
Published in: Journal of Intelligent Information Systems / Issue 1/2020
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-018-0525-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2020

Exploiting recommendation confidence in decision-aware recommender systems

Data-driven user behavioral modeling: from real-world behavior to knowledge, algorithms, and systems

Online social network analysis: detection of communities of interest

Topic and sentiment aware microblog summarization for twitter

Data-driven decision making in critique-based recommenders: from a critique to social media data

A knowledge-based multi-criteria collaborative filtering approach for discovering services in mobile cloud computing platforms

Premium Partner