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User’s Privacy in Recommendation Systems Applying Online Social Network Data: A Survey and Taxonomy

Aghasian, E ORCID: 0000-0002-5232-4934, Garg, S ORCID: 0000-0003-3510-2464 and Montgomery, J ORCID: 0000-0002-5360-7514 2019 , 'User’s Privacy in Recommendation Systems Applying Online Social Network Data: A Survey and Taxonomy', in O Khalid and SU Khan and AY Zomaya (eds.), Big Data Recommender Systems: Recent Trends and Advances , The Institution of Engineering and Technology, Stevenage, United Kingdom, pp. 1-26.

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Abstract

Recommender systems have become an integral part of many social networks and extract knowledge from a user’s personal and sensitive data both explicitly, with the user’s 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 Type: Book Section
Authors/Creators:Aghasian, E and Garg, S and Montgomery, J
Keywords: social network, recommender system
Publisher: The Institution of Engineering and Technology
Copyright Information:

Copyright 2019 The Institution of Engineering and Technology

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