De-amplifying Bias from Differential Privacy in Language Model Fine-tuning
Authors: Sanjari Srivastava, Piotr Mardziel, Zhikhun Zhang, Archana Ahlawat, Anupam Datta, John C Mitchell | Published: 2024-02-07
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Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models
Authors: Junjie Chu, Zeyang Sha, Michael Backes, Yang Zhang | Published: 2024-02-05 | Updated: 2024-10-07
Human-Centered Privacy Research in the Age of Large Language Models
Authors: Tianshi Li, Sauvik Das, Hao-Ping Lee, Dakuo Wang, Bingsheng Yao, Zhiping Zhang | Published: 2024-02-03
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
Authors: Xi Li, Jiaqi Wang | Published: 2024-02-02
BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
Authors: Hamed Poursiami, Ihsen Alouani, Maryam Parsa | Published: 2024-02-01 | Updated: 2024-05-07
Integrating Differential Privacy and Contextual Integrity
Authors: Sebastian Benthall, Rachel Cummings | Published: 2024-01-28
GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
Authors: Eugene Frimpong, Khoa Nguyen, Mindaugas Budzys, Tanveer Khan, Antonis Michalas | Published: 2024-01-26
MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption
Authors: Prajwal Panzade, Daniel Takabi, Zhipeng Cai | Published: 2024-01-17
Local Privacy-preserving Mechanisms and Applications in Machine Learning
Authors: Likun Qin, Tianshuo Qiu | Published: 2024-01-08
Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach
Authors: Mahtab Talaei, Iman Izadi | Published: 2024-01-04