ICLR 2024PastPrivacy & security
Privacy Regulation and Protection in Machine Learning
PML
- Submission deadline
- Feb 10, 2024, 13:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (28)
Fetched from OpenReview (v2) on 2026-06-10.
Balancing Privacy and Performance for Private Federated Learning Algorithms
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences
Cache Me If You Can: The Case For Retrieval Augmentation in Federated Learning
Communication-Efficient Differentially Private Federated Learning Using Second-Order Information
Confidential-DPproof : Confidential Proof of Differentially Private Training
Data Forging Is Harder Than You Think
Differentially Private Best Subset Selection Via Integer Programming
Differentially Private Latent Diffusion Models
DNA: Differential privacy Neural Augmentation for contact tracing
Efficient Language Model Architectures for Differentially Private Federated Learning
Efficient Private Federated Non-Convex Optimization With Shuffled Model
FairProof : Confidential and Certifiable Fairness for Neural Networks
Fed Up with Complexity: Simplifying Many-Task Federated Learning with NTKFedAvg
Federated Unlearning: a Perspective of Stability and Fairness
Gradient-Congruity Guided Federated Sparse Training
Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing
Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest
Langevin Unlearning
Linearizing Models for Efficient yet Robust Private Inference
Online Experimentation under Privacy Induced Identity Fragmentation
Personalized Differential Privacy for Ridge Regression
Posterior Probability-based Label Recovery Attack in Federated Learning
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Privacy-preserving data release leveraging optimal transport and particle gradient descent
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
The Privacy Power of Correlated Noise in Decentralized Learning
Understanding Practical Membership Privacy of Deep Learning
WAVES: Benchmarking the Robustness of Image Watermarks