arXiv:2604.20985v2 Announce Type: replace Abstract: In machine learning, privacy requirements at inference or deployment time often evolve due to changing policies, regulations, or user preferences. In this work, we aim to construct a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training, given a set of existing models trained on the same dataset with different privacy/utility tradeoffs. We propose two post-processing techniques, namely random selection and linear combination, to generate final private models satisfying any target privacy p
The increasing focus on data privacy regulations and ethical AI development is driving research into methods for maintaining privacy during AI model development and deployment.
This development offers a practical approach to building privacy-preserving AI systems without the need for extensive retraining, addressing a critical challenge for AI adoption in sensitive domains.
Organizations can now dynamically adjust the privacy guarantees of their deployed AI models to meet evolving requirements and regulations more efficiently, reducing development overhead.
- · AI-reliant sectors (healthcare, finance)
- · Privacy-focused tech companies
- · Data privacy regulators
- · Researchers in differential privacy
- · Organizations with rigid AI deployment pipelines
- · Methods requiring full model retraining for privacy updates
Easier compliance with privacy regulations like GDPR and CCPA for AI systems becomes possible.
Increased trust in AI applications, leading to broader adoption in privacy-sensitive areas.
The development of a marketplace for 'privacy-tuned' pre-trained models becomes a viable business model.
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Read at arXiv cs.LG