DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

Abstract

QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.

LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

“…This method integrates location-sensitive hashing (LSH) techniques. And Li et al [25] protect user privact by masking the initial QoS data by implementing a nonlinear conversion that retains the similarity among the QoS datasets.…”