arXiv:2407.04884v4 Announce Type: replace Abstract: The hidden state threat model of differential privacy (DP) assumes that the adversary has access only to the final trained machine learning (ML) model, without seeing intermediate states during training. However, the current privacy analyses under this model are restricted to convex optimization problems, reducing their applicability to multi-layer neural networks, which are essential in modern deep learning applications. Notably, the most successful applications of the hidden state privacy analyses in classification tasks have only been for
The continuous evolution of AI models, particularly multi-layer neural networks, necessitates advanced privacy protection methods, making research into differential privacy for these architectures critical.
This research addresses a key limitation in applying differential privacy to complex AI models, which is crucial for balancing model utility with data privacy in sensitive applications.
The ability to extend differential privacy guarantees to non-convex, multi-layer neural networks under the hidden state threat model expands the scope of privacy-preserving machine learning.
- · AI researchers
- · Organizations handling sensitive data
- · Users of AI applications
- · Adversaries exploiting intermediate AI model states
Improved privacy protections for advanced AI models in development and deployment.
Increased adoption of differential privacy techniques in deep learning applications across various industries.
Enhanced trust in AI systems handling personal or confidential information, potentially accelerating AI integration into highly regulated sectors.
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Read at arXiv cs.LG