In the realm of multimedia data analysis, the extensive use of image datasets has escalated concerns over privacy protection within such data. Current research predominantly focuses on privacy protection either in data sharing or upon the release of trained machine learning models. Our study pioneers a comprehensive privacy protection framework that safeguards image data privacy concurrently during data sharing and model publication. We propose an interactive image privacy protection framework that utilizes generative machine learning models to modify image information at the attribute level and employs machine unlearning algorithms for the privacy preservation of model parameters. This user-interactive framework allows for adjustments in privacy protection intensity based on user feedback on generated images, striking a balance between maximal privacy safeguarding and maintaining model performance. Within this framework, we instantiate two modules: a differential privacy diffusion model for protecting attribute information in images and a feature unlearning algorithm for efficient updates of the trained model on the revised image dataset. Our approach demonstrated superiority over existing methods on facial datasets across various attribute classifications.
TINA: Text-Free Inversion Attack for Unlearned Text-to-Image Diffusion Models
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；City University of Hong Kong（香港城市大学）；Shenzhen Loop Area Institute（深圳河套学院）；Peng Cheng Laboratory（鹏城实验室）；Shandong University（山东大学）
BlindU: Blind Machine Unlearning without Revealing Erasing Data
School of Computer Science, University of Technology Sydney（计算机科学学院，技术悉尼大学）；School of Cyber Science and Engineering, Southeast University（网络科学与工程学院，东南大学）
UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion
Huazhong University of Science and Technology（华中科技大学）；Institute of Guizhou Aerospace Measuring and Testing Technology（贵州航天测量测试技术研究院）；University of Technology Sydney（悉尼科技大学）；Griffith University（格里菲斯大学）
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Southeast University（东南大学）；Ant Group, Alibaba（蚂蚁集团）
