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Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study

Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
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Academic Editor: Andreas Holzinger
Mach. Learn. Knowl. Extr. 2022, 4(3), 591-620; https://doi.org/10.3390/make4030028
Received: 27 May 2022 / Revised: 15 June 2022 / Accepted: 19 June 2022 / Published: 22 June 2022
(This article belongs to the Section Learning)
Machine unlearning is the task of updating machine learning (ML) models after a subset of the training data they were trained on is deleted. Methods for the task are desired to combine effectiveness and efficiency (i.e., they should effectively “unlearn” deleted data, but in a way that does not require excessive computational effort (e.g., a full retraining) for a small amount of deletions). Such a combination is typically achieved by tolerating some amount of approximation in the unlearning. In addition, laws and regulations in the spirit of “the right to be forgotten” have given rise to requirements for certifiability (i.e., the ability to demonstrate that the deleted data has indeed been unlearned by the ML model). In this paper, we present an experimental study of the three state-of-the-art approximate unlearning methods for logistic regression and demonstrate the trade-offs between efficiency, effectiveness and certifiability offered by each method. In implementing this study, we extend some of the existing works and describe a common unlearning pipeline to compare and evaluate the unlearning methods on six real-world datasets and a variety of settings. We provide insights into the effect of the quantity and distribution of the deleted data on ML models and the performance of each unlearning method in different settings. We also propose a practical online strategy to determine when the accumulated error from approximate unlearning is large enough to warrant a full retraining of the ML model. View Full-Text
Keywords: machine unlearning; pipelines; logistic regression machine unlearning; pipelines; logistic regression
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MDPI and ACS Style

Mahadevan, A.; Mathioudakis, M. Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study. Mach. Learn. Knowl. Extr. 2022, 4, 591-620. https://doi.org/10.3390/make4030028

AMA Style

Mahadevan A, Mathioudakis M. Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study. Machine Learning and Knowledge Extraction. 2022; 4(3):591-620. https://doi.org/10.3390/make4030028

Chicago/Turabian Style

Mahadevan, Ananth, and Michael Mathioudakis. 2022. "Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study" Machine Learning and Knowledge Extraction 4, no. 3: 591-620. https://doi.org/10.3390/make4030028

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