arXiv Machine Learning
Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 4
Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD
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).
arXiv AI
Jun 17
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
arXiv:2503. 10945v3 Announce Type: replace-cross Abstract: Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture.
arXiv Machine Learning
Jun 3
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
arXiv:2605. 11170v2 Announce Type: replace Abstract: Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model utility, particularly for large-scale deletion requests.
arXiv Machine Learning
Aug 18
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
arXiv:2303. 07152v3 Announce Type: replace-cross Abstract: Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis.
Hugging Face Trending Papers
Jun 3
Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD
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.
arXiv Machine Learning
Jul 23
Differentially Private Neural Network Training Under the Hidden State Assumption
arXiv:2407. 08233v3 Announce Type: replace Abstract: Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-based approaches such as PATE suffer from data inefficiency due to disjoint data partitioning.