arXiv:2606.04384v1 Announce Type: new Abstract: Machine learning's reliance on sensitive data necessitates privacy-preserving techniques like Differentially Private Stochastic Gradient Descent (DPSGD). However, DPSGD suffers from substantial utility degradation and slow convergence due to gradient clipping and noise injection. Prior works have attempted to improve DPSGD from various perspectives; notably, the Differentially Private Selective Update and Release (DPSUR) algorithm has achieved remarkable model utility. However, the privacy accounting in DPSUR overlooks the variation in sampling p
The increasing reliance on sensitive data within machine learning necessitates continuous advancements in privacy-preserving techniques to address both regulatory and ethical concerns.
Improving the utility and convergence of differentially private stochastic gradient descent (DPSGD) is critical for wider adoption of privacy-preserving AI in real-world applications without sacrificing model performance.
New methods for privacy accounting in DPSGD, such as those building on DPSUR, promise to enhance the practical viability of privacy-preserving machine learning by balancing privacy guarantees with model utility.
- · AI/ML researchers
- · Data privacy solution providers
- · Industries handling sensitive data (e.g., healthcare, finance)
- · Organizations using less robust privacy techniques
- · Data exploiters
Further research into advanced privacy accounting and sampling techniques for DPSGD.
Increased trust and adoption of privacy-preserving AI models in commercial and public sectors due to improved utility.
Potential for new regulatory frameworks that mandate specific privacy-preserving methodologies, leveraging more sophisticated accounting methods.
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Read at arXiv cs.LG