Abstract
Recent advances in sensing and computing technologies have led to the rise of eye-tracking platforms. Ranging from mobiles to high-end mixed reality headsets, a wide spectrum of interactive systems now employs eye-tracking. However, eye gaze data is a rich source of sensitive information that can reveal an individual's physiological and psychological traits. Prior approaches to protecting eye-tracking data suffer from two major drawbacks: they are either incompatible with the current eye-tracking ecosystem or provide no formal privacy guarantee. In this paper, we propose Kalεido, an eye-tracking data processing system that (1) provides a formal privacy guarantee, (2) integrates seamlessly with existing eye-tracking ecosystems, and (3) operates in real-time. Kalεido acts as an intermediary protection layer in the software stack of eye-tracking systems. We conduct a comprehensive user study and trace-based analysis to evaluate Kalεido. Our user study shows that the users enjoy a satisfactory level of utility from Kalεido. Additionally, we present empirical evidence of Kalεido's effectiveness in thwarting real-world attacks on eye-tracking data.
| Original language | English |
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| Title of host publication | 30th USENIX Security Symposium (USENIX Security 21) |
| Publisher | USENIX Association |
| Pages | 1793-1810 |
| Number of pages | 18 |
| ISBN (Print) | 9781939133243 |
| Publication status | Published - 1 Aug 2021 |
| Event | 30th USENIX Security Symposium 2021 - Virtual event Duration: 11 Aug 2021 → 13 Aug 2021 Conference number: 30 https://www.usenix.org/conference/usenixsecurity21 |
Conference
| Conference | 30th USENIX Security Symposium 2021 |
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| Period | 11/08/21 → 13/08/21 |
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