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Privacy

363 directly classified papers

Papers per year

Papers

Large Language Models Can Be Contextual Privacy Protection Learners EMNLP 2024
Wasserstein Differential Privacy AAAI 2024
Optimal Locally Private Nonparametric Classification with Public Data JMLR 2024
Protecting Privacy in Classifiers by Token Manipulation ACL 2024
Noisy Neighbors: Efficient membership inference attacks against LLMs ACL 2024
Data Contamination Calibration for Black-box LLMs ACL 2024
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility AAAI 2024
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy ACL 2024
Locally Differentially Private In-Context Learning COLING 2024
Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models ACL 2024
Towards More Realistic Membership Inference Attacks on Large Diffusion Models WACV 2024
Disguise without Disruption: Utility-Preserving Face De-identification AAAI 2024
$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning NIPS 2024
Confidence Is All You Need for MI Attacks (Student Abstract) AAAI 2024
Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection WACV 2024
Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting EMNLP 2024
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models EMNLP 2024
Demystifying Verbatim Memorization in Large Language Models EMNLP 2024
User Inference Attacks on Large Language Models EMNLP 2024
Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility AAAI 2024
Concealing Sensitive Samples against Gradient Leakage in Federated Learning AAAI 2024
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification NIPS 2024
Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains EMNLP 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift NIPS 2024
Privacy Attacks on Schedule-Driven Data AAAI 2023