Differential privacy in collaborative filtering recommender systems: a review

Area of Science:

• Computer Science
• Information Retrieval
• Cybersecurity

Background:

• Recommender systems offer personalized content but raise privacy concerns due to user data utilization.
• Differential privacy is a common technique to protect user data by introducing noise, yet it often degrades recommendation accuracy.
• A significant trade-off exists between maintaining user privacy and ensuring high-quality recommendations.

Purpose of the Study:

• To provide a comprehensive overview of privacy threats in recommender systems.
• To introduce the differential privacy framework for safeguarding user data.
• To review and highlight research that enhances the balance between privacy and accuracy in recommender systems.

Main Methods:

• Overview of privacy vulnerabilities in recommender systems.
• Introduction to the principles and application of differential privacy.
• Systematic review of existing recommender system approaches employing differential privacy.
• Analysis of research focused on mitigating the accuracy-privacy trade-off.

Main Results:

• Identified key privacy threats inherent in recommender system architectures.
• Detailed the mechanisms through which differential privacy can be applied to protect user data.
• Cataloged various techniques aimed at improving recommendation quality under differential privacy constraints.
• Highlighted research directions for optimizing the privacy-utility balance.

Conclusions:

• Differential privacy is a viable, albeit imperfect, solution for privacy in recommender systems.
• Ongoing research is crucial for refining methods to improve the accuracy-privacy trade-off.
• Future work should address complex issues like privacy-fairness relationships and diverse user privacy needs.