Novel approach to deduplicate AI models trained with privacy-preserving techniques while maintaining strict privacy guarantees and reducing storage costs.

• Introduces a privacy-aware deduplication framework that identifies shared model components without compromising privacy budgets
• Achieves up to 80% reduction in storage requirements while preserving model utility
• Provides mathematical guarantees for privacy preservation across deduplicated model versions
• Enables organizations to efficiently manage multiple model versions with varying privacy-utility tradeoffs

This research is crucial for security teams deploying privacy-preserving ML at scale, allowing them to maintain compliance with privacy regulations while optimizing infrastructure costs.

Privacy and Accuracy-Aware AI/ML Model Deduplication