Location Privacy for Mobile Crowd Sensing through Population Mapping

Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users’ mobile phones to locate (Bluetooth-enabled) objects in their vici...

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Main Authors: Minho Shin, Cory Cornelius, Apu Kapadia, Nikos Triandopoulos, David Kotz
Format: Article
Language:English
Published: MDPI AG 2015-06-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/15/7/15285
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author Minho Shin
Cory Cornelius
Apu Kapadia
Nikos Triandopoulos
David Kotz
author_facet Minho Shin
Cory Cornelius
Apu Kapadia
Nikos Triandopoulos
David Kotz
author_sort Minho Shin
collection DOAJ
description Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users’ mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users’ privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces.
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spelling doaj.art-4ed12d07463a410e960f7af0aa5fa2af2022-12-22T02:19:49ZengMDPI AGSensors1424-82202015-06-01157152851531010.3390/s150715285s150715285Location Privacy for Mobile Crowd Sensing through Population MappingMinho Shin0Cory Cornelius1Apu Kapadia2Nikos Triandopoulos3David Kotz4Myongji University, Myongjiro 116, Yongin 449-728, KoreaIntel Labs, Hillsboro, OR 97124, USAIndiana University, Bloomington, IN 47408, USABoston University, 111 Cummington Mall, Boston, MA 02215, USADartmouth College, Hanover, NH 03755, USAOpportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users’ mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users’ privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces.http://www.mdpi.com/1424-8220/15/7/15285location privacyk-anonymitymobility traces
spellingShingle Minho Shin
Cory Cornelius
Apu Kapadia
Nikos Triandopoulos
David Kotz
Location Privacy for Mobile Crowd Sensing through Population Mapping
Sensors
location privacy
k-anonymity
mobility traces
title Location Privacy for Mobile Crowd Sensing through Population Mapping
title_full Location Privacy for Mobile Crowd Sensing through Population Mapping
title_fullStr Location Privacy for Mobile Crowd Sensing through Population Mapping
title_full_unstemmed Location Privacy for Mobile Crowd Sensing through Population Mapping
title_short Location Privacy for Mobile Crowd Sensing through Population Mapping
title_sort location privacy for mobile crowd sensing through population mapping
topic location privacy
k-anonymity
mobility traces
url http://www.mdpi.com/1424-8220/15/7/15285
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AT apukapadia locationprivacyformobilecrowdsensingthroughpopulationmapping
AT nikostriandopoulos locationprivacyformobilecrowdsensingthroughpopulationmapping
AT davidkotz locationprivacyformobilecrowdsensingthroughpopulationmapping