TY - JOUR
T1 - Location privacy for mobile crowd sensing through population mapping
AU - Shin, Minho
AU - Cornelius, Cory
AU - Kapadia, Apu
AU - Triandopoulos, Nikos
AU - Kotz, David
N1 - Publisher Copyright:
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
PY - 2015/6/29
Y1 - 2015/6/29
N2 - 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.
AB - 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.
KW - K-anonymity
KW - Location privacy
KW - Mobility traces
UR - http://www.scopus.com/inward/record.url?scp=84934780090&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84934780090&partnerID=8YFLogxK
U2 - 10.3390/s150715285
DO - 10.3390/s150715285
M3 - Article
AN - SCOPUS:84934780090
SN - 1424-8220
VL - 15
SP - 15285
EP - 15310
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 7
ER -