arXiv:2601.14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs. This renders private prediction, i.e., privatizing a model's outputs rather than its parameters, a natural privacy target: model outputs are lower-dimensional and far more stable to training-data changes than weights. While differential privacy (DP) cannot effectively exploit this as it calibrates noise to worst-case sensitivity that is intractable to bound for non-convex models, we argue that PAC privacy is a natural fit for private prediction. It is instance-based, and calibrates
The increasing deployment of machine learning models through APIs necessitates new approaches to ensuring privacy for model outputs, moving beyond traditional parameter-focused methods.
This development offers a practical method for private prediction, potentially accelerating the secure deployment of AI services and fostering greater trust in AI applications.
The focus for privacy in AI shifts from model parameters to model outputs, introducing PAC privacy as a viable alternative to differential privacy for real-world scenarios.
- · AI service providers
- · Cybersecurity sector
- · Users of AI APIs
- · Healthcare and finance AI
- · Companies with weak privacy practices
Wider adoption of privacy-preserving machine learning models, especially for sensitive data applications.
Reduced regulatory friction for deploying AI in privacy-sensitive sectors, accelerating innovation.
The emergence of new privacy-as-a-service offerings for AI models, creating a specialized market.
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Read at arXiv cs.LG