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Location Privacy for Mobile Crowd Sensing through Population Mapping.

Shin M, Cornelius C, Kapadia A, Triandopoulos N, Kotz D - Sensors (Basel) (2015)

Bottom Line: 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.The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter.

View Article: PubMed Central - PubMed

Affiliation: Myongji University, Myongjiro 116, Yongin 449-728, Korea. mhshin@mju.ac.kr.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Time slot duration vs. time slot start. (a) Average median k-accuracy; the line represents 95% k-accuracy; (b) average median cluster area; the line represents 1500 m2.
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f7-sensors-15-15285: Time slot duration vs. time slot start. (a) Average median k-accuracy; the line represents 95% k-accuracy; (b) average median cluster area; the line represents 1500 m2.

Mentions: Figure 7 shows how a chosen time slot start and duration affected k-accuracy and cluster size. Figures 7 show that as the time slot start moves from low population time (e.g., midnight) toward the high population time (e.g., 2 p.m.), the slot duration necessary to get high k-accuracy and a small cluster size decreases. We also observe an irregular increase of necessary slot duration due to the time slots spanning across consecutive days.


Location Privacy for Mobile Crowd Sensing through Population Mapping.

Shin M, Cornelius C, Kapadia A, Triandopoulos N, Kotz D - Sensors (Basel) (2015)

Time slot duration vs. time slot start. (a) Average median k-accuracy; the line represents 95% k-accuracy; (b) average median cluster area; the line represents 1500 m2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541831&req=5

f7-sensors-15-15285: Time slot duration vs. time slot start. (a) Average median k-accuracy; the line represents 95% k-accuracy; (b) average median cluster area; the line represents 1500 m2.
Mentions: Figure 7 shows how a chosen time slot start and duration affected k-accuracy and cluster size. Figures 7 show that as the time slot start moves from low population time (e.g., midnight) toward the high population time (e.g., 2 p.m.), the slot duration necessary to get high k-accuracy and a small cluster size decreases. We also observe an irregular increase of necessary slot duration due to the time slots spanning across consecutive days.

Bottom Line: 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.The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter.

View Article: PubMed Central - PubMed

Affiliation: Myongji University, Myongjiro 116, Yongin 449-728, Korea. mhshin@mju.ac.kr.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus