Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/210154
Title: Incentivizing Crowdsensing With Location-Privacy Preserving
Authors: Xiong Wang;Zhe Liu;Xiaohua Tian;Xiaoying Gan;Yunfeng Guan;Xinbing Wang
Year: 2017
Publisher: IEEE
Abstract: Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality, k-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for k-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and k-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.
URI: http://localhost/handle/Hannan/210154
volume: 16
issue: 10
More Information: 6940,
6952
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8002606.pdf4.96 MBAdobe PDF
Title: Incentivizing Crowdsensing With Location-Privacy Preserving
Authors: Xiong Wang;Zhe Liu;Xiaohua Tian;Xiaoying Gan;Yunfeng Guan;Xinbing Wang
Year: 2017
Publisher: IEEE
Abstract: Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality, k-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for k-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and k-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.
URI: http://localhost/handle/Hannan/210154
volume: 16
issue: 10
More Information: 6940,
6952
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8002606.pdf4.96 MBAdobe PDF
Title: Incentivizing Crowdsensing With Location-Privacy Preserving
Authors: Xiong Wang;Zhe Liu;Xiaohua Tian;Xiaoying Gan;Yunfeng Guan;Xinbing Wang
Year: 2017
Publisher: IEEE
Abstract: Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality, k-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for k-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and k-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.
URI: http://localhost/handle/Hannan/210154
volume: 16
issue: 10
More Information: 6940,
6952
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8002606.pdf4.96 MBAdobe PDF