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Userís Privacy in Recommendation Systems Applying Online Social Network Data: A Survey and Taxonomy


Aghasian, E and Garg, S and Montgomery, J, User's Privacy in Recommendation Systems Applying Online Social Network Data: A Survey and Taxonomy, Big Data Recommender Systems - Volume 1: Algorithms, Architectures, Big Data, Security and Trus, Institution of Engineering and Technology, O Khalid, SU Khan, and AY Zomaya (ed), Stevenage, United Kingdom, pp. 259-282. ISBN 978-1-78561-501-6 (2019) [Research Book Chapter]

Copyright Statement

Copyright 2019 The Institution of Engineering and Technology

DOI: doi:10.1049/PBPC035F_ch12


Recommender systems have become an integral part of many social networks and extract knowledge from a userís personal and sensitive data both explicitly, with the userís knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.

Item Details

Item Type:Research Book Chapter
Keywords:social network, recommender system
Research Division:Information and Computing Sciences
Research Group:Cybersecurity and privacy
Research Field:Cybersecurity and privacy not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Information services not elsewhere classified
UTAS Author:Aghasian, E (Mr Erfan Aghasian)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:126773
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2018-06-22
Last Modified:2020-07-22
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