Summary of From Machine Learning to Machine Unlearning: Complying with Gdpr’s Right to Be Forgotten While Maintaining Business Value Of Predictive Models, by Yuncong Yang et al.
From Machine Learning to Machine Unlearning: Complying with GDPR’s Right to be Forgotten while Maintaining Business Value of Predictive Models
by Yuncong Yang, Xiao Han, Yidong Chai, Reza Ebrahimi, Rouzbeh Behnia, Balaji Padmanabhan
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel framework called Ensemble-based iTerative Information Distillation (ETID) to efficiently erase specific training data from well-trained predictive models while maintaining model performance. The ETID framework incorporates an ensemble learning method to build an accurate predictive model and a distillation-based unlearning method tailored to the constructed ensemble model. This approach is designed to facilitate handling data erasure requests in compliance with recent privacy regulations like GDPR’s “Right to Be Forgotten” (RTBF). Experimental results demonstrate that ETID outperforms state-of-the-art methods in terms of efficiency and quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps companies comply with the “Right to Be Forgotten” (RTBF) by developing a new way to erase training data from their predictive models. The method, called ETID, uses two techniques: one to build an accurate model and another to erase the unwanted data. This approach is faster and better than existing methods, which can help companies avoid financial losses. |
Keywords
» Artificial intelligence » Distillation » Ensemble model
