New paper accepted at PETS

We are pleased to announce that the following paper was accepted at the Privacy Enhancing Technologies Symposium (PETS).

Congratulations to the authors!

1. Gaze3P: Gaze-Based Prediction of User-Perceived Privacy

   Mayar Elfares, Pascal Reisert, Ralf Küsters, Andreas Bulling

   Proc. Privacy Enhancing Technologies Symposium (PETS), 2026.

   Abstract Links BibTeX Project

   Privacy is a highly subjective concept and perceived variably by different individuals. Previous research on quantifying user-perceived privacy has primarily relied on questionnaires. Furthermore, applying user-perceived privacy to optimise the parameters of privacy-preserving techniques (PPT) remains insufficiently explored. To address these limitations, we introduce Gaze3P - the first dataset specifically designed to facilitate systematic investigations into user-perceived privacy. Our dataset comprises gaze data from 100 participants and 1,000 stimuli, encompassing a range of private and safe attributes. With Gaze3P we train a machine learning model to implicitly and dynamically predict perceived privacy from human eye gaze. Through comprehensive experiments, we show that the resulting models achieve high accuracy. Finally, we illustrate how predicted privacy can be used to optimise the parameters of differentially private mechanisms, thereby enhancing their alignment with user expectations.

   doi:

   @inproceedings{elfares26_pets, title = {{{Gaze3P}}: {{Gaze-Based Prediction}} of {{User-Perceived Privacy}}}, shorttitle = {{{Gaze3P}}}, author = {Elfares, Mayar and Reisert, Pascal and K{\"u}sters, Ralf and Bulling, Andreas}, booktitle = {Proc. Privacy Enhancing Technologies Symposium (PETS)}, year = {2026}, doi = {} }

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