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Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies
Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguar...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155614/ https://www.ncbi.nlm.nih.gov/pubmed/34056584 http://dx.doi.org/10.3389/fdata.2021.624424 |
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author | Sharma, Aakash Nilsen, Thomas Bye Czerwinska, Katja Pauline Onitiu, Daria Brenna, Lars Johansen, Dag Johansen, Håvard D. |
author_facet | Sharma, Aakash Nilsen, Thomas Bye Czerwinska, Katja Pauline Onitiu, Daria Brenna, Lars Johansen, Dag Johansen, Håvard D. |
author_sort | Sharma, Aakash |
collection | PubMed |
description | Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using gossips. We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance. |
format | Online Article Text |
id | pubmed-8155614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81556142021-05-28 Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies Sharma, Aakash Nilsen, Thomas Bye Czerwinska, Katja Pauline Onitiu, Daria Brenna, Lars Johansen, Dag Johansen, Håvard D. Front Big Data Big Data Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using gossips. We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance. Frontiers Media S.A. 2021-05-13 /pmc/articles/PMC8155614/ /pubmed/34056584 http://dx.doi.org/10.3389/fdata.2021.624424 Text en Copyright © 2021 Sharma, Nilsen, Czerwinska, Onitiu, Brenna, Johansen and Johansen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Sharma, Aakash Nilsen, Thomas Bye Czerwinska, Katja Pauline Onitiu, Daria Brenna, Lars Johansen, Dag Johansen, Håvard D. Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies |
title | Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies |
title_full | Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies |
title_fullStr | Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies |
title_full_unstemmed | Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies |
title_short | Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies |
title_sort | up-to-the-minute privacy policies via gossips in participatory epidemiological studies |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155614/ https://www.ncbi.nlm.nih.gov/pubmed/34056584 http://dx.doi.org/10.3389/fdata.2021.624424 |
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