DP-SGD faces fundamental privacy-utility trade-off limitations

A new research paper published on arXiv details fundamental limitations in Differentially Private Stochastic Gradient Descent (DP-SGD), a common method for private model training. The study, analyzing DP-SGD under the $f$-differential privacy framework, demonstrates that achieving both strong privacy and high utility simultaneously is challenging. The findings indicate that enforcing robust privacy necessitates a significant increase in noise, which in turn degrades model accuracy, presenting a bottleneck for DP-SGD under standard adversarial assumptions. AI

IMPACT Highlights a significant bottleneck in achieving both privacy and utility in machine learning model training.

RANK_REASON Academic paper detailing theoretical limitations of a machine learning technique. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• DP-SGD
• f-Differential Privacy
• Gaussian function
• Murat Bilgehan Ertan

• paper
• safety

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