Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy

Summary

The paper presents a unified framework for multimodal 3D human pose estimation that fuses RGB, LiDAR and mmWave radar data while addressing subject-level privacy. It introduces a black-box subject membership inference attack, analyzed with empirical pointwise maximal leakage, and user-level differential privacy via Action Temporal Stratification, a population-weighted within-subject sampling strategy. The framework is evaluated on the MM-Fi dataset across three experimental protocols.

Related items

• InfoA novel privacy-preserving large language model integrating trust-weighted and ethical gradient maskingSimilar attack · OpenAlex (peer-reviewed AI security)
• InfoGDPFDL: A secure and efficient personalized federated distillation framework against inference attacksSimilar attack · Elsevier Security Journals
• InfoBenchmarking post-processing methods in local differential privacy for utility and adversarial robustnessSimilar attack · Elsevier Security Journals
• InfoDeepU: Deeper Granular Within-Layer Machine UnlearningSimilar attack · IEEE Xplore (Security & AI Journals)
• InfoPrivacy-Preserving GAN for Synthetic Data against Membership Inference AttackSimilar attack · ACM Digital Library (TOPS, DTRAP, CSUR)