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- W4281621667 abstract "Eye-tracking is a critical source of information for understanding human behavior and developing future mixed-reality technology. Eye-tracking enables applications that classify user activity or predict user intent. However, eye-tracking datasets collected during common virtual reality tasks have also been shown to enable unique user identification, which creates a privacy risk. In this paper, we focus on the problem of user re-identification from eye-tracking features. We adapt standardized privacy definitions of k-anonymity and plausible deniability to protect datasets of eye-tracking features, and evaluate performance against re-identification by a standard biometric identification model on seven VR datasets. Our results demonstrate that re-identification goes down to chance levels for the privatized datasets, even as utility is preserved to levels higher than 72% accuracy in document type classification." @default.
- W4281621667 created "2022-06-13" @default.
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- W4281621667 date "2022-06-08" @default.
- W4281621667 modified "2023-09-23" @default.
- W4281621667 title "For Your Eyes Only: Privacy-preserving eye-tracking datasets" @default.
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- W4281621667 doi "https://doi.org/10.1145/3517031.3529618" @default.
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