@inproceedings{400ff51eb5ab4c9ca072059ee35aec19,
title = "Privacy-preserving collaborative web services QoS prediction via differential privacy",
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",
author = "Shushu Liu and An Liu and Zhixu Li and Guanfeng Liu and Jiajie Xu and Lei Zhao and Kai Zheng",
year = "2017",
doi = "10.1007/978-3-319-63579-8_16",
language = "English",
isbn = "9783319635781",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "200--214",
editor = "Lei Chen and Jensen, {Christian S.} and Cyrus Shahabi and Xiaochun Yang and Xiang Lian",
booktitle = "Wen and Big Data",
address = "United States",
note = "1st Joint International Conference on APWeb-WAIM ; Conference date: 07-07-2017 Through 09-07-2017",
}