Anonymization Techniques for Behavioral Biometric Data: A Survey

ACM Computing Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

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 health conditions. With more tracking techniques (e.g. fitness trackers, mixed reality) entering our everyday lives of captured processed. Hence, to protect individuals’ privacy against unwanted inferences are required, before such data To consolidate knowledge in this area, the first systematically review suggested anonymization for behavioral data. We taxonomize compare existing solutions regarding goals, conceptual operation, advantages, limitations. categorization allows comparison across different traits. traits voice, gait, hand motions, eye gaze, heartbeat (ECG), brain activity (EEG). analysis shows that some (e.g., voice) have received much attention, while others activity) mostly neglected. find evaluation methodology further improved.

Language: Английский

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PrivXR: A Cross-Platform Privacy-Preserving API and Privacy Panel for Extended Reality

2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Journal Year: 2024, Volume and Issue: unknown, P. 417 - 420

Published: March 11, 2024

Extended Reality (XR) technologies are rising in popularity and affordability, while including more sensors for new features at each generation, thus becoming increasingly pervasive. While this shapes up interconnected experiences across Augmented (AR), Mixed (MR), Virtual (VR) devices, it also introduces privacy threats users, especially related to their sensible biometric data. Despite various propositions privacy-enhancing tailored XR academia, there still not enough options end-users be informed about risks act upon them. Therefore, we present a work-in-progress solution, consisting of user-friendly panel, cross-platform privacy-preserving Application Programming Interface (API). Our solution aims provide awareness potential XR, enabling users define access features, better protecting by modifying the input Eventually, aim work become viable choice current future generations devices—especially context cross-platform, multi-user experiences, which expected norm.

Language: Английский

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Effect of Data Degradation on Motion Re-Identification

Published: June 4, 2024

The use of virtual and augmented reality devices is increasing, but these sensor-rich pose risks to privacy. ability track a user's motion infer the identity or characteristics user poses privacy risk that has received significant attention. Existing deep-network-based defenses against this risk, however, require amounts training data have not yet been shown generalize beyond specific applications. In work, we study effect signal degradation on identifiability, specifically through added noise, reduced framerate, precision, dimensionality data. Our experiment shows state-of-the-art identification attacks still achieve near-perfect accuracy for each degradations. This negative result demonstrates difficulty anonymizing gives some justification existing data- compute-intensive deep-network based methods.

Language: Английский

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Cloud-based secure human action recognition with fully homomorphic encryption

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Oct. 16, 2024

Language: Английский

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