Safeguarding Identity: A Comprehensive Survey of Anonymization Strategies for Behavioral Biometric Data

• Shane T. Aldrich

  Department of Computer Engineering, Politecnico di Milano, Italy

  Author

• Kathy R. Smith

  School of Computer Science and Applied Mathematics, University of the Witwatersrand, South Africa

  Author

Behavioral biometrics, encompassing unique patterns in human actions like gait, keystroke dynamics, voice, and eye movements, offer powerful tools for authentication and interaction. However, the rich, often sensitive information embedded in this data poses significant privacy risks, as it can inadvertently reveal personal attributes such as gender, age, health conditions, or emotional states. This article presents a comprehensive survey of anonymization techniques specifically designed for behavioral biometric data. It categorizes existing approaches based on modality (voice, gait, keystroke dynamics, ECG, EEG, eye movements, and gesture recognition) and strategy (data transformation, perturbation, and generative models). The discussion highlights the inherent trade-off between achieving strong privacy guarantees and maintaining sufficient data utility for intended applications. By reviewing the state-of-the-art and identifying persistent challenges, this survey aims to inform future research and foster the development of truly privacy-enhanced behavioral biometric systems.

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