Anonymization Techniques for Behavioral Biometric Data: A Survey

Simon Hanisch, Patricia Arias Cabarcos, Javier Parra-Arnau, Thorsten Strufe

Abstract: 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 such as emotions and health conditions. With more and more behavior tracking techniques (e.g., fitness trackers, mixed reality) entering our everyday lives, more of our behavior is captured and processed. Hence, techniques to protect individuals’ privacy against unwanted inferences are required before such data is processed. To consolidate knowledge in this area, we are the first to systematically review suggested anonymization techniques for behavioral biometric data. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our categorization allows for the comparison of anonymization techniques across different behavioral biometric traits. 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.

External IDs:dblp:journals/csur/HanischCPS25