Privacy for Vision & Imaging

The focus of this workshop is to bring together researchers from industry and academia that focus on both distributed and privacy-preserved machine learning for vision and imaging. These topics are of increasingly large commercial and policy interest. It is therefore important to build a community for this research area, which involves collaborating researchers that share insights, code, data, benchmarks, training pipelines, etc and together aim to improve the state of privacy in computer vision. The topics of interest for this workshop include, but are not limited to:

• Notions of privacy for computer vision and imaging
• Privacy and security in other imaging modalities
• Privacy-preserving synthetic data release
• Privacy-preserving video understanding
• Privacy-Enhancing Face Biometrics
• Privacy in Medical Imaging
• Federated Learning and Split Learning
• Differential Privacy in Deep Learning and Computer Vision
• Privacy and security attacks (Model Inversion, Membership Inference etc.)
• Metrics and Benchmarks for analysing privacy risks in computer vision
• Differential privacy and other statistical notions of privacy: theory, applications, and implementations
• Hardware-based techniques for privacy-preserving ML
• Cryptographic techniques for Privacy in vision & imaging
• Policy and Compliance for Data Privacy
• Privacy, Fairness, Accountability and Transparency (F.A.T) in Machine Learning
• Applications of privacy-preserving ML