Journal ArticleDOI
Privacy Preserving Location-Aware Personalized Web Service Recommendations
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TLDR
This paper develops a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users that is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information.Abstract:
The personalized Web service recommendation based on Quality of Service (QoS) is gaining increasing popularity due to its promising ability to help users find high quality services. Studies suggest that it is beneficial to use Collaborative Filtering (CF)-based techniques to facilitate Web service recommendations which can achieve high accuracy in predicting the QoS for unobserved Web services. With the QoS, location of users and Web services has been another significant factor in predicting the QoS values. The more factors that are available to the service providers, the more accurate predictions can be generated. However these factors are privacy sensitive and therefore it is risky to disclose them to any third party service provider. To address this challenge, in this paper we develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. Our protocol is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information. We conduct extensive experimental analysis on publicly available data sets and prove that our method is both secure and practical.read more
Citations
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Journal ArticleDOI
A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph
TL;DR: This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system.
Journal ArticleDOI
A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues.
A. S. M. Kayes,Rudri Kalaria,Iqbal H. Sarker,Md. Saiful Islam,Paul A. Watters,Alex Hay-Man Ng,Mohammad Hammoudeh,Shahriar Badsha,Indika Kumara +8 more
TL;DR: A new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework is proposed, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices.
Journal ArticleDOI
Web Service QoS Prediction via Collaborative Filtering: A Survey
TL;DR: This survey summarizes and analyzes the state-of-the-art CF QoS prediction approaches of Web services and discusses their features and differences.
Journal ArticleDOI
Achieving security scalability and flexibility using Fog-Based Context-Aware Access Control
TL;DR: A new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework is proposed to enable flexible access control data from multiple sources to investigate the limitations of current fog-based access control and the trade-off between latency and processing overheads is considered.
Proceedings ArticleDOI
Privacy Preserving Cyber Threat Information Sharing and Learning for Cyber Defense
TL;DR: A privacy preserving decision tree algorithm is proposed, where each organization can build and learn the decision tree based on overall organizations’ training spam/ham email data without disclosing any private information of any party.
References
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Journal ArticleDOI
QoS-Aware Web Service Recommendation by Collaborative Filtering
TL;DR: This paper proposes a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users, and shows that the algorithm achieves better prediction accuracy than other approaches.
Proceedings ArticleDOI
Personalized QoS Prediction forWeb Services via Collaborative Filtering
TL;DR: Experimental results demonstrate that a collaborative filtering based approach to making similarity mining and prediction from consumers' experiences can make significant improvement on the effectiveness of QoS prediction for web services.
Proceedings ArticleDOI
WSRec: A Collaborative Filtering Based Web Service Recommender System
TL;DR: The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches, and includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web Service QoS value prediction.