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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

Target k vs. time slot start.
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f6-sensors-15-15285: Target k vs. time slot start.

Mentions: Figure 6 shows how a chosen target k and time slot start affecting k-accuracy and cluster size. In Figure 6a, we see that the k-accuracy is relatively stable regardless of the chosen time slot start. This is desirable, as it implies that the same level of privacy is maintained throughout different time slots in a day. However, the cluster size showed notable differences among different slot times. Depending on the selected time slot start, Figure 6b shows us that for a given time slot and target k, the cluster size increased for those times when the population is relatively inactive. The cluster size, on the contrary, decreases as the time slot moves toward high population periods. During high population times, around noon for example, smaller clusters can meet the privacy requirement, resulting in better utility.


Location Privacy for Mobile Crowd Sensing through Population Mapping.

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

Target k vs. time slot start.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-15-15285: Target k vs. time slot start.
Mentions: Figure 6 shows how a chosen target k and time slot start affecting k-accuracy and cluster size. In Figure 6a, we see that the k-accuracy is relatively stable regardless of the chosen time slot start. This is desirable, as it implies that the same level of privacy is maintained throughout different time slots in a day. However, the cluster size showed notable differences among different slot times. Depending on the selected time slot start, Figure 6b shows us that for a given time slot and target k, the cluster size increased for those times when the population is relatively inactive. The cluster size, on the contrary, decreases as the time slot moves toward high population periods. During high population times, around noon for example, smaller clusters can meet the privacy requirement, resulting in better utility.

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