Publication
Title
Exploring unlearning methods to ensure the privacy, security, and usability of recommender systems
Author
Abstract
Machine learning algorithms have proven highly effective in analyzing large amounts of data and identifying complex patterns and relationships. One application of machine learning that has received significant attention in recent years is recommender systems, which are algorithms that analyze user behavior and other data to suggest items or content that a user may be interested in. However useful, these systems may unintentionally retain sensitive, outdated, or faulty information. Posing a risk to user privacy, system security, and limiting a system’s usability. In this research proposal, we aim to address these challenges by investigating methods for machine “unlearning”, which would allow information to be efficiently “forgotten” or “unlearned” from machine learning models. The main objective of this proposal is to develop the foundation for future machine unlearning methods. We first evaluate current unlearning methods and explore novel adversarial attacks on these methods’ verifiability, efficiency, and accuracy to gain new insights and further develop the theory of machine unlearning. Using our gathered insights, we seek to create novel unlearning methods that are verifiable, efficient, and limit unnecessary accuracy degradation. Through this research, we seek to make significant contributions to the theoretical foundations of machine unlearning while also developing unlearning methods that can be applied to real-world problems.
Language
English
Source (book)
RecSys '23 : proceedings of the 17th ACM Conference on Recommender Systems, September 18-22, 2023, Singapore
Publication
New York, N.Y. : Association for Computing Machinery , 2023
ISBN
979-84-00-70241-9
DOI
10.1145/3604915.3608862
Volume/pages
p. 1300-1304
ISI
001156630300183
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 06.11.2023
Last edited 08.05.2024
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