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Breaking the Trilemma of Privacy, Utility, and Efficiency via Controllable Machine Unlearning

Authors:

Ziwei ZhuAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 1260 - 1271

https://doi.org/10.1145/3589334.3645669

Published: 13 May 2024 Publication History

Abstract

Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influence of specific data samples on a given model without the need to retrain it from scratch. Accordingly, existing methods focus on maximizing user privacy protection. However, there are different degrees of privacy regulations for each real-world web-based application. Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios. Furthermore, designing the MU algorithm with simple control of the aforementioned trade-off is desirable but challenging due to the inherent complex interaction. To address the challenges, we present Controllable Machine Unlearning (ConMU), a novel framework designed to facilitate the calibration of MU. The ConMU framework contains three integral modules: an important data selection module that reconciles the runtime efficiency and model generalization, a progressive Gaussian mechanism module that balances privacy and model generalization, and an unlearning proxy that controls the trade-offs between privacy and runtime efficiency. Comprehensive experiments on various benchmark datasets have demonstrated the robust adaptability of our control mechanism and its superiority over established unlearning methods. ConMU explores the full spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners to account for different real-world regulations. Source code available at: https://github.com/guangyaodou/ConMU

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Cited By

Liu ZJiang YShen JPeng MLam KYuan XLiu X(2024)A Survey on Federated Unlearning: Challenges, Methods, and Future DirectionsACM Computing Surveys10.1145/367901457:1(1-38)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3679014
Ouyang ZZhang CHou SMa SChen CLi TXiao XZhang CYe Y(2024)Symbolic Prompt Tuning Completes the App Promotion GraphMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_12(183-198)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70381-2_12

Index Terms

Breaking the Trilemma of Privacy, Utility, and Efficiency via Controllable Machine Unlearning
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Human and societal aspects of security and privacy

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Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 ACM.

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Published: 13 May 2024

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Cited By

Liu ZJiang YShen JPeng MLam KYuan XLiu X(2024)A Survey on Federated Unlearning: Challenges, Methods, and Future DirectionsACM Computing Surveys10.1145/367901457:1(1-38)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3679014
Ouyang ZZhang CHou SMa SChen CLi TXiao XZhang CYe Y(2024)Symbolic Prompt Tuning Completes the App Promotion GraphMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_12(183-198)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70381-2_12

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