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