mmSLP: Semantic-Level Privacy Protection for mmWave Human Activity Recognition

Millimeter-wave (mmWave) radar enables privacy-friendly human activity recognition (HAR) without visual sensing. However, mmWave-based HAR may still reveal sensitive behaviors, raising privacy concerns for users. Therefore, we aim to reduce the sensitive-activity recognition capability of HAR systems. We observe that HAR systems commonly require user-provided data for fine-tuning, and these data remain under the user’s control before sharing. We therefore apply carefully designed perturbations to these data before fine-tuning to reduce the recognition accuracy of sensitive activities. Based on this idea, we propose mmSLP, a semantic-level privacy protection framework for mmWave HAR. Experiments show that mmSLP reduces sensitive-activity accuracy from 85.41% to 42.12%, while maintaining normal-activity accuracy at 88.09% compared with 84.89% under clean fine-tuning, with low computational overhead.