Privacy Adhering Machine Un-learning in NLP

• URL: http://arxiv.org/abs/2212.09573v1
• Date: Mon, 19 Dec 2022 16:06:45 GMT
• Title: Privacy Adhering Machine Un-learning in NLP
• Authors: Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, Dan Roth
• Abstract summary: In real world industry use Machine Learning to build models on user data. Such mandates require effort both in terms of data as well as model retraining. continuous removal of data and model retraining steps do not scale. We propose textitMachine Unlearning to tackle this challenge.
• Score: 66.17039929803933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Regulations introduced by General Data Protection Regulation (GDPR) in the EU or California Consumer Privacy Act (CCPA) in the US have included provisions on the \textit{right to be forgotten} that mandates industry applications to remove data related to an individual from their systems. In several real world industry applications that use Machine Learning to build models on user data, such mandates require significant effort both in terms of data cleansing as well as model retraining while ensuring the models do not deteriorate in prediction quality due to removal of data. As a result, continuous removal of data and model retraining steps do not scale if these applications receive such requests at a very high frequency. Recently, a few researchers proposed the idea of \textit{Machine Unlearning} to tackle this challenge. Despite the significant importance of this task, the area of Machine Unlearning is under-explored in Natural Language Processing (NLP) tasks. In this paper, we explore the Unlearning framework on various GLUE tasks \cite{Wang:18}, such as, QQP, SST and MNLI. We propose computationally efficient approaches (SISA-FC and SISA-A) to perform \textit{guaranteed} Unlearning that provides significant reduction in terms of both memory (90-95\%), time (100x) and space consumption (99\%) in comparison to the baselines while keeping model performance constant.

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