Real-Time Lightweight Gaze Privacy-Preservation Techniques Validated via Offline Gaze-Based Interaction Simulation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914155841191936 |
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| author | Raju, Mehedi Hasan Komogortsev, Oleg V. |
| author_facet | Raju, Mehedi Hasan Komogortsev, Oleg V. |
| contents | This study examines the effectiveness of the real-time privacy-preserving techniques through an offline gaze-based interaction simulation framework. Those techniques aim to reduce the amount of identity-related information in eye-tracking data while improving the efficacy of the gaze-based interaction. Although some real-time gaze privatization methods were previously explored, their validation on the large dataset was not conducted. We propose a functional framework that allows to study the efficacy of real-time gaze privatization on an already collected offline dataset. The key metric used to assess the reduction of identity-related information is the identification rate, while improvements in gaze-based interactions are evaluated through signal quality during interaction. Our additional contribution is the employment of an extremely lightweight Kalman filter framework that reduces the amount of identity-related information in the gaze signal and improves gaze-based interaction performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Lightweight Gaze Privacy-Preservation Techniques Validated via Offline Gaze-Based Interaction Simulation Raju, Mehedi Hasan Komogortsev, Oleg V. Human-Computer Interaction Cryptography and Security This study examines the effectiveness of the real-time privacy-preserving techniques through an offline gaze-based interaction simulation framework. Those techniques aim to reduce the amount of identity-related information in eye-tracking data while improving the efficacy of the gaze-based interaction. Although some real-time gaze privatization methods were previously explored, their validation on the large dataset was not conducted. We propose a functional framework that allows to study the efficacy of real-time gaze privatization on an already collected offline dataset. The key metric used to assess the reduction of identity-related information is the identification rate, while improvements in gaze-based interactions are evaluated through signal quality during interaction. Our additional contribution is the employment of an extremely lightweight Kalman filter framework that reduces the amount of identity-related information in the gaze signal and improves gaze-based interaction performance. |
| title | Real-Time Lightweight Gaze Privacy-Preservation Techniques Validated via Offline Gaze-Based Interaction Simulation |
| topic | Human-Computer Interaction Cryptography and Security |
| url | https://arxiv.org/abs/2511.09846 |