arXiv:2606.05435v1 Announce Type: new Abstract: Differentially private stochastic gradient descent (DP-SGD) has become the standard framework for privacy-preserving machine learning, yet its reliance on a fixed gradient clipping threshold to limit sensitivity remains a significant practical limitation. Adaptive clipping algorithms such as AdaClip shift and scale the gradient prior to clipping and adding noise so that the clipped gradient yields a more informative descent direction. The shift and scaling parameters are selected adaptively based on the empirical mean and variance. However, in ex
The increasing deployment of machine learning in sensitive applications necessitates robust privacy guarantees, driving continuous innovation in techniques like Differential Privacy (DP-SGD).
Improving the practical efficacy of differentially private machine learning is crucial for wider adoption, enabling the use of sensitive data for AI development while mitigating privacy risks.
This research outlines an advancement in adaptive clipping and momentum for DP-SGD, making privacy-preserving machine learning more effective by improving model utility without sacrificing privacy guarantees.
- · Organizations handling sensitive data
- · Privacy-preserving AI researchers
- · Enterprises deploying P-ML models
- · Attackers attempting to extract private data from ML models
- · Current less efficient DP-SGD implementations
Increased adoption of privacy-preserving machine learning frameworks across various industries.
Development of new AI applications that were previously unfeasible due to privacy concerns.
Potential for regulatory frameworks to mandate stronger privacy-preserving methods as they become more practical.
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Read at arXiv cs.LG