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Differential Privacy for Eye-Tracking Data

URN to cite this document:
Liu, Ao ; Xia, Lirong ; Duchowski, Andrew ; Bailey, Reynold ; Holmqvist, Kenneth ; Jain, Eakta
Date of publication of this fulltext: 03 Feb 2020 08:43


As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of ...


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