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

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posted on 2023-05-24, 05:37 authored by Aghasian, E, Saurabh GargSaurabh Garg, James MontgomeryJames Montgomery
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.

History

Publication title

Big Data Recommender Systems - Volume 1: Algorithms, Architectures, Big Data, Security and Trus

Editors

O Khalid, SU Khan, and AY Zomaya

Pagination

259-282

ISBN

978-1-78561-501-6

Department/School

School of Information and Communication Technology

Publisher

Institution of Engineering and Technology

Place of publication

Stevenage, United Kingdom

Extent

14

Rights statement

Copyright 2019 The Institution of Engineering and Technology

Repository Status

  • Restricted

Socio-economic Objectives

Information services not elsewhere classified

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    University Of Tasmania

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