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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. probability p.
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f5-sensors-15-15285: Target k vs. probability p.

Mentions: Figure 5 shows how a chosen target k and probability p affected k-accuracy and the median cluster size. In general, if target k increases, we enlarge each cluster to observe more users therein, and thus, we have higher k-accuracy (as u(C) becomes higher). The same is expected as the probability p increases. Figure 5a clearly shows the positive correlation between k (or p) and k-accuracy. The line in the figure represents where k-accuracy is 95%. Note that a probability of 70% or higher yields k-accuracies of 95% or better regardless of the chosen target k. This is so because we may end up with a larger cluster size than the ideal cluster size to meet the probability criteria. Figure 5b also shows the positive correlation between k (or p) and the median cluster size. These two graphs combined, we can determine parameters k and p based on the privacy goal (k-accuracy) and the utility goal (cluster size).


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. probability p.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-15-15285: Target k vs. probability p.
Mentions: Figure 5 shows how a chosen target k and probability p affected k-accuracy and the median cluster size. In general, if target k increases, we enlarge each cluster to observe more users therein, and thus, we have higher k-accuracy (as u(C) becomes higher). The same is expected as the probability p increases. Figure 5a clearly shows the positive correlation between k (or p) and k-accuracy. The line in the figure represents where k-accuracy is 95%. Note that a probability of 70% or higher yields k-accuracies of 95% or better regardless of the chosen target k. This is so because we may end up with a larger cluster size than the ideal cluster size to meet the probability criteria. Figure 5b also shows the positive correlation between k (or p) and the median cluster size. These two graphs combined, we can determine parameters k and p based on the privacy goal (k-accuracy) and the utility goal (cluster size).

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