File(s) under permanent embargo
User’s Privacy in Recommendation Systems Applying Online Social Network Data: A Survey and Taxonomy
chapter
posted on 2023-05-24, 05:37 authored by Aghasian, E, Saurabh GargSaurabh Garg, Erin MontgomeryErin MontgomeryRecommender 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 TrusEditors
O Khalid, SU Khan, and AY ZomayaPagination
259-282ISBN
978-1-78561-501-6Department/School
School of Information and Communication TechnologyPublisher
Institution of Engineering and TechnologyPlace of publication
Stevenage, United KingdomExtent
14Rights statement
Copyright 2019 The Institution of Engineering and TechnologyRepository Status
- Restricted