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An investigation of privacy preservation in deep learning-based eye-tracking
BACKGROUND: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data, raise concerns about the potential leak of ident...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469631/ https://www.ncbi.nlm.nih.gov/pubmed/36100851 http://dx.doi.org/10.1186/s12938-022-01035-1 |
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author | Seyedi, Salman Jiang, Zifan Levey, Allan Clifford, Gari D. |
author_facet | Seyedi, Salman Jiang, Zifan Levey, Allan Clifford, Gari D. |
author_sort | Seyedi, Salman |
collection | PubMed |
description | BACKGROUND: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data, raise concerns about the potential leak of identifiable data. In this work, we focus on the case of a deep network model trained on images of individual faces. MATERIALS AND METHODS: A previously published deep learning model, trained to estimate the gaze from full-face image sequences was stress tested for personal information leakage by a white box inference attack. Full-face video recordings taken from 493 individuals undergoing an eye-tracking- based evaluation of neurological function were used. Outputs, gradients, intermediate layer outputs, loss, and labels were used as inputs for a deep network with an added support vector machine emission layer to recognize membership in the training data. RESULTS: The inference attack method and associated mathematical analysis indicate that there is a low likelihood of unintended memorization of facial features in the deep learning model. CONCLUSIONS: In this study, it is showed that the named model preserves the integrity of training data with reasonable confidence. The same process can be implemented in similar conditions for different models. |
format | Online Article Text |
id | pubmed-9469631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94696312022-09-14 An investigation of privacy preservation in deep learning-based eye-tracking Seyedi, Salman Jiang, Zifan Levey, Allan Clifford, Gari D. Biomed Eng Online Research BACKGROUND: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data, raise concerns about the potential leak of identifiable data. In this work, we focus on the case of a deep network model trained on images of individual faces. MATERIALS AND METHODS: A previously published deep learning model, trained to estimate the gaze from full-face image sequences was stress tested for personal information leakage by a white box inference attack. Full-face video recordings taken from 493 individuals undergoing an eye-tracking- based evaluation of neurological function were used. Outputs, gradients, intermediate layer outputs, loss, and labels were used as inputs for a deep network with an added support vector machine emission layer to recognize membership in the training data. RESULTS: The inference attack method and associated mathematical analysis indicate that there is a low likelihood of unintended memorization of facial features in the deep learning model. CONCLUSIONS: In this study, it is showed that the named model preserves the integrity of training data with reasonable confidence. The same process can be implemented in similar conditions for different models. BioMed Central 2022-09-13 /pmc/articles/PMC9469631/ /pubmed/36100851 http://dx.doi.org/10.1186/s12938-022-01035-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Seyedi, Salman Jiang, Zifan Levey, Allan Clifford, Gari D. An investigation of privacy preservation in deep learning-based eye-tracking |
title | An investigation of privacy preservation in deep learning-based eye-tracking |
title_full | An investigation of privacy preservation in deep learning-based eye-tracking |
title_fullStr | An investigation of privacy preservation in deep learning-based eye-tracking |
title_full_unstemmed | An investigation of privacy preservation in deep learning-based eye-tracking |
title_short | An investigation of privacy preservation in deep learning-based eye-tracking |
title_sort | investigation of privacy preservation in deep learning-based eye-tracking |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469631/ https://www.ncbi.nlm.nih.gov/pubmed/36100851 http://dx.doi.org/10.1186/s12938-022-01035-1 |
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