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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

A (10, 0.7)-map generated for the 12 p.m.–1 p.m. time slot. Each colored region means that on 70% of the days, there were 10 or more unique associations between the hours of 12 p.m.–1 p.m. for each day between 22 September 2009 and 1 October 2009. The black dots correspond to AP locations.
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f4-sensors-15-15285: A (10, 0.7)-map generated for the 12 p.m.–1 p.m. time slot. Each colored region means that on 70% of the days, there were 10 or more unique associations between the hours of 12 p.m.–1 p.m. for each day between 22 September 2009 and 1 October 2009. The black dots correspond to AP locations.

Mentions: Figure 4 shows an example of a (10, 0.7)-map generated from 10 days of history at the 12 p.m.–1 p.m. time slot. The points within the tiles indicate the locations of access points that were clustered together for that tile. There are 182 tiles in Figure 4, the smallest and largest being 89 m2 and 6,869,523 m2, with a median area of 2306 m2 (quartiles Q1 = 708 m2, Q3 = 7849 m2).Note that the tiles (and hence, clusters) near the edges of the map tend to have a large area, because we do not crop the tiles to the campus area.


Location Privacy for Mobile Crowd Sensing through Population Mapping.

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

A (10, 0.7)-map generated for the 12 p.m.–1 p.m. time slot. Each colored region means that on 70% of the days, there were 10 or more unique associations between the hours of 12 p.m.–1 p.m. for each day between 22 September 2009 and 1 October 2009. The black dots correspond to AP locations.
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-15-15285: A (10, 0.7)-map generated for the 12 p.m.–1 p.m. time slot. Each colored region means that on 70% of the days, there were 10 or more unique associations between the hours of 12 p.m.–1 p.m. for each day between 22 September 2009 and 1 October 2009. The black dots correspond to AP locations.
Mentions: Figure 4 shows an example of a (10, 0.7)-map generated from 10 days of history at the 12 p.m.–1 p.m. time slot. The points within the tiles indicate the locations of access points that were clustered together for that tile. There are 182 tiles in Figure 4, the smallest and largest being 89 m2 and 6,869,523 m2, with a median area of 2306 m2 (quartiles Q1 = 708 m2, Q3 = 7849 m2).Note that the tiles (and hence, clusters) near the edges of the map tend to have a large area, because we do not crop the tiles to the campus area.

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