Privacy Preserving Eye-tracking Applications
Description
The astounding advancements of technologies have not only brought benefits, but have also introduced a range of potential risks. Among the most pressing issues is the critical challenge of privacy leakage, which requires urgent attention and systematic investigation. As a golden standard of biometrics, eye data is particularly vulnerable to privacy attacks. Unlike other biometrics, eye behavior is more pervasive, harder to control, and often related to subconsciousness. Eye movements also provide hints into users’ mental status and health condition, both physically and psychologically. Hence, in the context of XR, eye privacy is even more threatened, since eye-based interaction lays the foundation of HCI in virtual and augmented environments.
We aim at privacy-preserving applications of eye tracking using different techniques including differential privacy, federated learning, domain adaptation, randomized encryption and many more techniques. An emphasis of our work is the application in virtual environments. Part of this work will be integrated into our VR classroom project.
Funding
- Deutsche Forschungsgemeinschaft (DFG) - Project number 491966293
Tags: Privacy-Preserving, Eye-Tracking, Biometrics
Papers
Mengdi Wang, Efe Bozkir, and Enkelejda Kasneci. 2025. Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study. The 25th International Conference on Digital Signal Processing (DSP 2025).
Mengdi Wang, Efe Bozkir, and Enkelejda Kasneci. 2025. Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer. Proceedings of the ACM on Computer Graphics and Interactive Techniques (2025). https://doi.org/10.1145/3729413
Süleyman Özdel, Efe Bozkir, and Enkelejda Kasneci. 2024. Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking. Proceedings of the ACM on Human-Computer Interaction, Volume 8, Issue ETRA (2024). https://doi.org/10.1145/3655605
Efe Bozkir, Süleyman Özdel, Mengdi Wang, Brendan David-John, Hong Gao, Kevin Butler, Eakta Jain, and Enkelejda Kasneci. 2023. Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges. arXiv:2305.14080. https://doi.org/10.48550/arXiv.2305.14080
Efe Bozkir, Onur Günlü, Wolfgang Fuhl, Rafael F. Schaefer, and Enkelejda Kasneci. 2021. Differential privacy for eye tracking with temporal correlations. Plos one 16, 8 (2021), e0255979. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255979
Wolfgang Fuhl, Efe Bozkir, and Enkelejda Kasneci. 2021. Reinforcement learning for the privacy preservation and manipulation of eye tracking data. In Artificial Neural Networks and Machine Learning–ICANN 2021: 30th International Conference on Artificial Neural Networks. https://arxiv.org/pdf/2002.06806.pdf
Efe Bozkir, Ali Burak Ünal, Mete Akgün, Enkelejda Kasneci, and Nico Pfeifer. 2020. Privacy Preserving Gaze Estimation Using Synthetic Images via a Randomized Encoding Based Framework. In ACM Symposium on Eye Tracking Research and Applications. https://doi.org/10.1145/3379156.3391364
Efe Bozkir, David Geisler, and Enkelejda Kasneci. 2019. Person independent, privacy preserving, and real time assessment of cognitive load using eye tracking in a virtual reality setup. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). https://www.hci.uni-tuebingen.de/assets/pdf/publications/EDE032019.pdf