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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Differential privacy in collaborative filtering recommender systems: a review.

Peter Müllner^1,2, Elisabeth Lex², Markus Schedl^3,4

¹Know-Center Gmbh, Graz, Austria.

Frontiers in Big Data

|October 30, 2023

Summary

Recommender systems risk user privacy. Differential privacy protects data but reduces recommendation quality. This review explores methods to balance privacy and accuracy in these systems.

Keywords:

accuracy-privacy trade-off collaborative filtering differential privacy recommender systems review

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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.