Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/629452
Title: Privacy-Aware High-Quality Map Generation with Participatory Sensing
Authors: Xi Chen;Xiaopei Wu;Xiang-Yang Li;Xiaoyu Ji;Yuan He;Yunhao Liu
subject: participatory sensing;map generation;privacy protection;curve reconstruction;data suppression
Year: 2016
Publisher: IEEE
Abstract: Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols that rely on users to provide their traces have been proposed. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. To be specific, the individual users merely need to upload unorganized sparse location points to reduce the risk of exposing users' traces and utilize the Crust, a technique from computational geometry for curve reconstruction, to estimate the unobserved map as well as evaluate the degree of privacy leakage. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is less than 10 m, even when the collected locations are about 32 m apart after clustering for the purpose of removing noise.
Description: 
URI: http://localhost/handle/Hannan/158338
http://localhost/handle/Hannan/629452
ISSN: 1536-1233
volume: 15
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7084117.pdf1.36 MBAdobe PDFThumbnail
Preview File
Title: Privacy-Aware High-Quality Map Generation with Participatory Sensing
Authors: Xi Chen;Xiaopei Wu;Xiang-Yang Li;Xiaoyu Ji;Yuan He;Yunhao Liu
subject: participatory sensing;map generation;privacy protection;curve reconstruction;data suppression
Year: 2016
Publisher: IEEE
Abstract: Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols that rely on users to provide their traces have been proposed. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. To be specific, the individual users merely need to upload unorganized sparse location points to reduce the risk of exposing users' traces and utilize the Crust, a technique from computational geometry for curve reconstruction, to estimate the unobserved map as well as evaluate the degree of privacy leakage. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is less than 10 m, even when the collected locations are about 32 m apart after clustering for the purpose of removing noise.
Description: 
URI: http://localhost/handle/Hannan/158338
http://localhost/handle/Hannan/629452
ISSN: 1536-1233
volume: 15
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7084117.pdf1.36 MBAdobe PDFThumbnail
Preview File
Title: Privacy-Aware High-Quality Map Generation with Participatory Sensing
Authors: Xi Chen;Xiaopei Wu;Xiang-Yang Li;Xiaoyu Ji;Yuan He;Yunhao Liu
subject: participatory sensing;map generation;privacy protection;curve reconstruction;data suppression
Year: 2016
Publisher: IEEE
Abstract: Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols that rely on users to provide their traces have been proposed. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. To be specific, the individual users merely need to upload unorganized sparse location points to reduce the risk of exposing users' traces and utilize the Crust, a technique from computational geometry for curve reconstruction, to estimate the unobserved map as well as evaluate the degree of privacy leakage. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is less than 10 m, even when the collected locations are about 32 m apart after clustering for the purpose of removing noise.
Description: 
URI: http://localhost/handle/Hannan/158338
http://localhost/handle/Hannan/629452
ISSN: 1536-1233
volume: 15
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7084117.pdf1.36 MBAdobe PDFThumbnail
Preview File