User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

TL;DR

This survey reviews privacy concerns, measurements, and techniques in social network-based recommender systems, highlighting the technical challenges and recent advancements in protecting user data.

Contribution

It provides a comprehensive taxonomy and overview of privacy issues and solutions in social network recommender systems, based on historical and recent research.

Findings

Identifies key privacy concerns in social network recommender systems.

Summarizes privacy-preserving techniques and their effectiveness.

Highlights technical difficulties in implementing privacy measures.

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.