PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization
Authors: Yidan Wang, Yanan Cao, Yubing Ren, Fang Fang, Zheng Lin, Binxing Fang | Published: 2025-05-15
Privacy Protection in Machine Learning
SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
Authors: Prajwal Panzade, Daniel Takabi | Published: 2022-04-11 | Updated: 2022-09-02
Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities
Authors: Esma Mansouri-Benssassi, Simon Rogers, Jim Smith, Felix Ritchie, Emily Jefferson | Published: 2021-11-10 | Updated: 2022-08-20
Data privacy protection in microscopic image analysis for material data mining
Authors: Boyuan Ma, Xiang Yin, Xiaojuan Ban, Haiyou Huang, Neng Zhang, Hao Wang, Weihua Xue | Published: 2021-11-09
Adaptive Machine Unlearning
Authors: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites | Published: 2021-06-08
Black-box Model Inversion Attribute Inference Attacks on Classification Models
Authors: Shagufta Mehnaz, Ninghui Li, Elisa Bertino | Published: 2020-12-07
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
Authors: Ali Burak Ünal, Mete Akgün, Nico Pfeifer | Published: 2020-12-04
Unleashing the Tiger: Inference Attacks on Split Learning
Authors: Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi | Published: 2020-12-04 | Updated: 2021-11-04
SSGD: A safe and efficient method of gradient descent
Authors: Jinhuan Duan, Xianxian Li, Shiqi Gao, Jinyan Wang, Zili Zhong | Published: 2020-12-03 | Updated: 2021-04-26
Privacy-preserving Data Sharing on Vertically Partitioned Data
Authors: Razane Tajeddine, Joonas Jälkö, Samuel Kaski, Antti Honkela | Published: 2020-10-19 | Updated: 2022-09-02