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Privacy

363 directly classified papers

Papers per year

Papers

Finding ε and δ of Traditional Disclosure Control Systems AAAI 2024
The Relative Gaussian Mechanism and its Application to Private Gradient Descent AISTATS 2024
The Role of Over-Parameterization in Machine Learning – the Good, the Bad, the Ugly AAAI 2024
Concealing Sensitive Samples against Gradient Leakage in Federated Learning AAAI 2024
Federated Experiment Design under Distributed Differential Privacy AISTATS 2024
$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning NIPS 2024
Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection WACV 2024
Unlearning during Learning: An Efficient Federated Machine Unlearning Method IJCAI 2024
Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility AAAI 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling NIPS 2024
Confidence Is All You Need for MI Attacks (Student Abstract) AAAI 2024
SAME: Sample Reconstruction against Model Extraction Attacks AAAI 2024
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data AISTATS 2024
High-Fidelity Gradient Inversion in Distributed Learning AAAI 2024
Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise AAAI 2024
Differentially Private Graph Diffusion with Applications in Personalized PageRanks NIPS 2024
Would You Like Your Data to Be Trained? A User Controllable Recommendation Framework AAAI 2024
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy ACL 2024
WARDEN: Multi-Directional Backdoor Watermarks for Embedding-as-a-Service Copyright Protection ACL 2024
Data Contamination Calibration for Black-box LLMs ACL 2024
Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication AAAI 2024
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy AAAI 2024
Analysis of Differentially Private Synthetic Data: A Measurement Error Approach AAAI 2024
Noisy Neighbors: Efficient membership inference attacks against LLMs ACL 2024
Wasserstein Differential Privacy AAAI 2024