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AE-IPP: Adversarial Example Enabled Identity Privacy Preserving With mmWave Signals
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DOI：10.1109/tmc.2026.3698565.png)
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Abstract
En 中文
Despite convenience and reliability of mmWave-based action recognition, it still raises privacy concerns on identity leakage threat since human behaviors could meanwhile expose massive user information in real-world applications. Existing solutions attempt to send anonymized features extracted from mmWave signals; however, features not only reduce the application flexibility but also increase the privacy disclosure risk due to original data reconstruction. Instead, in this paper we propose a de-identification system, AE-IPP, which customizes learned noises into the raw data to generate adversarial examples for identity privacy and action utility balance. In other words, the noises are sample-specific perturbations that are automatically learned for each sample through our presented network. To achieve the performance balance and ensure robustness to other models, we are faced with two challenges, including the decoupling of action and identity information and the transferability of models. To this end, AE-IPP focuses on respective attention areas by leveraging task-specific gradients and designs a dynamic attention mechanism to update the attention weights according to the final optimization objective. Moreover, we present a multidirectional perturbation strategy to improve the model generalization capabilities, enabling robust de-identification. Extensive experiments on mmWave datasets demonstrate the superiority of our method over state-of-the-art approaches.
Keywords:
Action recognition
privacy preserving
dynamic attention mechanism
multidirectional perturbation strategy
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