| Differentially Private Training of Mixture of Experts Models | Feb 11, 2024 | Computational EfficiencyMixture-of-Experts | —Unverified | 0 |
| Differentially Private Variational Dropout | Nov 30, 2017 | Privacy Preserving | —Unverified | 0 |
| Defending Label Inference Attacks in Split Learning under Regression Setting | Aug 18, 2023 | Federated LearningPrivacy Preserving | —Unverified | 0 |
| Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey | Feb 16, 2022 | FairnessPrivacy Preserving | —Unverified | 0 |
| Differential Privacy and Machine Learning: a Survey and Review | Dec 24, 2014 | BIG-bench Machine LearningPrivacy Preserving | —Unverified | 0 |
| Mitigating Statistical Bias within Differentially Private Synthetic Data | Aug 24, 2021 | Privacy Preserving | —Unverified | 0 |
| A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model | May 7, 2024 | Privacy Preserving | —Unverified | 0 |
| Defending against Reconstruction Attack in Vertical Federated Learning | Jul 21, 2021 | Federated LearningPrivacy Preserving | —Unverified | 0 |
| Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation | Mar 19, 2025 | Federated LearningPrivacy Preserving | —Unverified | 0 |
| AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs | May 22, 2024 | Privacy PreservingQuantization | —Unverified | 0 |
