New research explores high-dimensional asymptotics for private transfer learning and PCA

Two new research papers explore the high-dimensional asymptotics of differential privacy in machine learning. The first paper focuses on private transfer learning, proposing a weighted ridge estimator that uses only summary statistics to decide when external datasets are useful without direct access, while ensuring privacy guarantees. The second paper analyzes differentially private Principal Component Analysis (PCA), providing sharp asymptotic characterizations of its utility and privacy loss in the high-dimensional limit by combining hypothesis-testing formulations with contiguity arguments. AI

IMPACT These papers advance the theoretical understanding of privacy-preserving machine learning in high-dimensional settings, potentially enabling more robust and secure data analysis techniques.

RANK_REASON Two academic papers published on arXiv detailing theoretical advancements in differentially private machine learning techniques.

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• differentially private Principal Component Analysis (PCA)
• Gotit.pub
• High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning
• High-Dimensional Asymptotics of Differentially Private PCA
• $ ho$-zero-concentrated differential privacy
• Hugging Face
• Le Cam
• private transfer learning
• Roth
• ScienceCast
• weighted ridge estimator
• Youngjoo Yun

• paper
• safety

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