Privacy-Protecting Techniques for Behavioral Biometric Data: A Survey
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Abstract (arXiv)
Our behavior (the way we talk, walk, act or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions and health conditions. Hence, techniques to protect individuals privacy against unwanted inferences are required, if such data is planned to be processed. To consolidate knowledge in this area, we systematically review applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. We review anonymization techniques for the behavioral biometric traits of voice, gait, hand motions, eye-gaze, heartbeat (ECG), and brain activity (EEG). Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brain activity) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Sep 2021.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2023A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learningcited by 32
- 2025Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspectivecited by 0
- 2026Face De-Identification: A Domain-Centric Survey from Capture to Processingcited by 0
- 2026Face De-Identification: A Domain-Centric Survey from Capture to Processing28 Jul 2026
- 2025Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective16 Jul 2025
- 2023A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning10 Dec 2023