Exploring privacy concerns in news recommender systems

With the increasing ubiquity of access to online news sources, the news recommender systems are becoming widely popular in recent days. However, providing interesting news for each user is a challenging task in highly-dynamic news domain. Many news aggregator sites such as Google News suggest its users to provide sign in to the system for getting user-specific (relevant) news articles. For more generic news recommendation, the system collects user click history and page access pattern implicitly. Often the users are not sure about the usage of the collected and consolidated data by the recommender systems which they usually trade for receiving the news recommendation. Privacy of user identity, user behavior in terms of page access patterns contributes to the overall privacy risks in the news domain. This review paper discusses the current state-of-the-art of privacy risks and existing privacy preserving approaches in the news domain from user perspective.