Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.17126 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910724929880064 |
|---|---|
| author | Yang, Yuncong Han, Xiao Chai, Yidong Ebrahimi, Reza Behnia, Rouzbeh Padmanabhan, Balaji |
| author_facet | Yang, Yuncong Han, Xiao Chai, Yidong Ebrahimi, Reza Behnia, Rouzbeh Padmanabhan, Balaji |
| contents | Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from their well-trained predictive models. While researchers have introduced machine unlearning methods aimed at fast data erasure, these approaches often overlook maintaining model performance (e.g., accuracy), which can lead to financial losses and non-compliance with RTBF obligations. This work develops a holistic machine learning-to-unlearning framework, called Ensemble-based iTerative Information Distillation (ETID), to achieve efficient data erasure while preserving the business value of predictive models. ETID incorporates a new ensemble learning method to build an accurate predictive model that can facilitate handling data erasure requests. ETID also introduces an innovative distillation-based unlearning method tailored to the constructed ensemble model to enable efficient and effective data erasure. Extensive experiments demonstrate that ETID outperforms various state-of-the-art methods and can deliver high-quality unlearned models with efficiency. We also highlight ETID's potential as a crucial tool for fostering a legitimate and thriving market for data and predictive services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models Yang, Yuncong Han, Xiao Chai, Yidong Ebrahimi, Reza Behnia, Rouzbeh Padmanabhan, Balaji Machine Learning Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from their well-trained predictive models. While researchers have introduced machine unlearning methods aimed at fast data erasure, these approaches often overlook maintaining model performance (e.g., accuracy), which can lead to financial losses and non-compliance with RTBF obligations. This work develops a holistic machine learning-to-unlearning framework, called Ensemble-based iTerative Information Distillation (ETID), to achieve efficient data erasure while preserving the business value of predictive models. ETID incorporates a new ensemble learning method to build an accurate predictive model that can facilitate handling data erasure requests. ETID also introduces an innovative distillation-based unlearning method tailored to the constructed ensemble model to enable efficient and effective data erasure. Extensive experiments demonstrate that ETID outperforms various state-of-the-art methods and can deliver high-quality unlearned models with efficiency. We also highlight ETID's potential as a crucial tool for fostering a legitimate and thriving market for data and predictive services. |
| title | From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.17126 |