Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Authors: Rachel Redberg, Antti Koskela, Yu-Xiang Wang | Published: 2023-12-31
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Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
Authors: Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao, Shan Zhong, Matt White, Meikang Qiu | Published: 2023-12-29 | Updated: 2024-06-02
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms
Authors: Sofia Zahri, Hajar Bennouri, Ahmed M. Abdelmoniem | Published: 2023-12-24
SODA: Protecting Proprietary Information in On-Device Machine Learning Models
Authors: Akanksha Atrey, Ritwik Sinha, Saayan Mitra, Prashant Shenoy | Published: 2023-12-22
Robustness, Efficiency, or Privacy: Pick Two in Machine Learning
Authors: Youssef Allouah, Rachid Guerraoui, John Stephan | Published: 2023-12-22 | Updated: 2024-03-11
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
Authors: Clément Pierquin, Aurélien Bellet, Marc Tommasi, Matthieu Boussard | Published: 2023-12-21 | Updated: 2024-06-10
Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
Authors: Keyi Ju, Xiaoqi Qin, Hui Zhong, Xinyue Zhang, Miao Pan, Baoling Liu | Published: 2023-12-18 | Updated: 2024-03-07
Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models
Authors: Jiawei Zhao, Kejiang Chen, Xiaojian Yuan, Yuang Qi, Weiming Zhang, Nenghai Yu | Published: 2023-12-15 | Updated: 2024-10-10
Guardians of Trust: Navigating Data Security in AIOps through Vendor Partnerships
Authors: Subhadip Kumar | Published: 2023-12-10 | Updated: 2024-01-16
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
Authors: Jianwei Li, Sheng Liu, Qi Lei | Published: 2023-12-10 | Updated: 2024-03-15