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Tytuł:
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Privacy-preserving shared collaborative web services QoS prediction.
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Autorzy:
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Liu, An
Shen, Xindi
Xie, Haoran
Li, Zhixu
Liu, Guanfeng
Xu, Jiajie
Zhao, Lei
Wang, Fu Lee
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Temat:
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WEB services
DATA privacy
QUALITY of service
ELECTION forecasting
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Źródło:
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Journal of Intelligent Information Systems; Feb2020, Vol. 54 Issue 1, p205-224, 20p
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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. [ABSTRACT FROM AUTHOR]
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