ArticlePloS one2021
Differential privacy for eye tracking with temporal correlations.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Mapping Eye-Tracking Research in Human-Computer Interaction: A Science-Mapping and Content-Analysis Study.Journal of eye movement research · 2026Review
- A Clinician's Guide to Sharing Data for AI in Ophthalmology.Investigative ophthalmology & visual science · 2024Review
- Blockchain-based privacy and security model for transactional data in large private networks.Scientific reports · 2023Article
- A Survey on Differential Privacy for Medical Data Analysis.Annals of data science · 2023Review
- Clear Aligners and Smart Eye Tracking Technology as a New Communication Strategy between Ethical and Legal Issues.Life (Basel, Switzerland) · 2023Article
- Mobile eye tracking applied as a tool for customer experience research in a crowded train station.Journal of eye movement research · 2023Article
- An investigation of privacy preservation in deep learning-based eye-tracking.Biomedical engineering online · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.