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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

An example tessellation of all access points (APs)
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f1-sensors-15-15285: An example tessellation of all access points (APs)

Mentions: We choose the Voronoi tessellation with respect to the Wi-Fi access point (AP) locations in the map as the unit tessellation. The AP-based Voronoi tessellation partitions the map into as many tiles as there are access points, and (except at the border) each tile is roughly as large as the coverage of that AP. The Voronoi tessellation divides the map into tiles, each containing one AP, such that any MN in a tile connects to the network through the AP associated with the tile. See Figure 1 for an example of AP-based tessellation. We denote an AP-based unit tessellation by = {t1, t2, …, tn} where n is the number of APs, or tiles.


Location Privacy for Mobile Crowd Sensing through Population Mapping.

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

An example tessellation of all access points (APs)
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-15-15285: An example tessellation of all access points (APs)
Mentions: We choose the Voronoi tessellation with respect to the Wi-Fi access point (AP) locations in the map as the unit tessellation. The AP-based Voronoi tessellation partitions the map into as many tiles as there are access points, and (except at the border) each tile is roughly as large as the coverage of that AP. The Voronoi tessellation divides the map into tiles, each containing one AP, such that any MN in a tile connects to the network through the AP associated with the tile. See Figure 1 for an example of AP-based tessellation. We denote an AP-based unit tessellation by = {t1, t2, …, tn} where n is the number of APs, or tiles.

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