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Differentially Private Mobile Crowd Sensing Considering Sensing Errors

An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable res...

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Detalles Bibliográficos
Autores principales: Sei, Yuichi, Ohsuga, Akihiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285772/
https://www.ncbi.nlm.nih.gov/pubmed/32422958
http://dx.doi.org/10.3390/s20102785
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author Sei, Yuichi
Ohsuga, Akihiko
author_facet Sei, Yuichi
Ohsuga, Akihiko
author_sort Sei, Yuichi
collection PubMed
description An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered.
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spelling pubmed-72857722020-06-15 Differentially Private Mobile Crowd Sensing Considering Sensing Errors Sei, Yuichi Ohsuga, Akihiko Sensors (Basel) Article An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered. MDPI 2020-05-14 /pmc/articles/PMC7285772/ /pubmed/32422958 http://dx.doi.org/10.3390/s20102785 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sei, Yuichi
Ohsuga, Akihiko
Differentially Private Mobile Crowd Sensing Considering Sensing Errors
title Differentially Private Mobile Crowd Sensing Considering Sensing Errors
title_full Differentially Private Mobile Crowd Sensing Considering Sensing Errors
title_fullStr Differentially Private Mobile Crowd Sensing Considering Sensing Errors
title_full_unstemmed Differentially Private Mobile Crowd Sensing Considering Sensing Errors
title_short Differentially Private Mobile Crowd Sensing Considering Sensing Errors
title_sort differentially private mobile crowd sensing considering sensing errors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285772/
https://www.ncbi.nlm.nih.gov/pubmed/32422958
http://dx.doi.org/10.3390/s20102785
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