arXiv:2605.11170v2 Announce Type: replace Abstract: Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model utility, particularly for large-scale deletion requests. While leveraging public data is a standard technique in differential privacy to relax this tension, its role in unlearning remains unexplored. We address this gap by introducing Asymmetric Langevin Unlearning (ALU), a framework that uses public data to mitigate privacy costs. We prove that public data injection suppresses the unlearning cos
The increasing scale and complexity of AI models, coupled with growing regulatory and ethical demands for data privacy, highlight the urgent need for efficient machine unlearning techniques.
This research introduces a method that could significantly improve the trade-off between privacy and utility in AI systems, making unlearning more practical for large-scale applications and fostering broader AI adoption.
The ability to unlearn data without crippling model performance could enable AI systems to more readily comply with privacy regulations and improve data governance practices.
- · AI developers
- · Privacy-focused industries
- · Cloud service providers
- · Regulatory bodies
- · Malicious actors exploiting data
- · Companies with poor data governance
Machine unlearning becomes more feasible and widely adopted in commercial AI products.
Increased trust in AI systems due to better data privacy and compliance capabilities.
New business models emerge around certified unlearning services and privacy-preserving AI architectures.
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Read at arXiv cs.LG