An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
arXiv:2605.20521v1 Announce Type: new Abstract: Fine-tuning adapts a pretrained machine learning model to a small, sensitive dataset, but this process risks memorizing individual new data points, making the model vulnerable to adversaries who seek to extract sensitive information. In this work, we develop a randomized algorithm based on the exponential mechanism for fine-tuning while ensuring differential privacy. Our key idea is to construct a simple utility function that combines a local quadratic approximation of the pretrained model with information from the new dataset. The resulting expo
The increasing deployment of AI in sensitive applications and the rise of privacy concerns are driving rapid research into robust privacy-preserving techniques.
This research provides a concrete, algorithmic solution for fine-tuning machine learning models while safeguarding sensitive data, directly addressing a critical privacy and security vulnerability in AI development.
The ability to fine-tune AI models on private datasets with explicit privacy guarantees reduces the risk of data leakage and increases trust in AI applications working with sensitive information.
- · Healthcare sector
- · Financial services
- · AI developers focused on privacy
- · Individuals with sensitive data
- · Adversaries attempting data extraction
- · Models without privacy guarantees
- · Organizations with weak data governance
Increased adoption of privacy-preserving AI fine-tuning methods across industries.
New standards and regulations emerging around differentially private fine-tuning for sensitive AI applications.
The development of a competitive market for privacy-enhanced AI models and services.
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Read at arXiv cs.LG