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  1. Proceedings of the 1st international workshop on Mobile location-based service (MLBS '11)
  2. History trajectory privacy-preserving through graph partition
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Learning techniques in social and location-based service recommendation
Extractable mobile photo tags
Toward better indoor localization: cooperative localization and estimation fusion
Efficient relative camera orientation detection for mobile applications
Location-based topic evolution
Guided by touch: tactile pedestrian navigation
Fusion of Wi-Fi and bluetooth for indoor localization
Social linking and physical proximity in a mobile location-based service
Inertial sensors aided image alignment and stitching for panorama on mobile phones
Temporal sensitivity for location disclosure through mobile photo-sharing
Preference-based top-k spatial keyword queries
History trajectory privacy-preserving through graph partition
XINS: the anatomy of an indoor positioning and navigation architecture
Privacy-aware mobile location-based systems

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History trajectory privacy-preserving through graph partition

Content Provider ACM Digital Library
Author Huang, Yi Meng, Xiaofeng Huo, Zheng
Abstract With rapid development of positioning techniques and location based services (LBS), locations and traces of moving objects are collected by service providers, the data will then be published for novel applications. Although analyzing and mining trajectories is useful for mobility-related applications, new challenges of trajectory privacy leakage arise accordingly. Trajectories contain rich spatio-temporal history information that may expose users' whereabouts and other personal privacy. At present, trajectory k-anonymity which aims at anonymizing k trajectories together on all sample points is one of the most popular techniques to protect trajectory privacy. The challenge lies in how to find trajectory k-anonymity sets. In this paper, a trajectory graph is constructed to simulate spatial relations of trajectories, based on which we propose to find trajectory k-anonymity sets through graph partition, which is proven NP-complete. We then propose a greedy partition method to find trajectory k-anonymity sets, as well as yielding low information loss. We run a series of experiments on both real-world and synthetic datasets, the results show the effectiveness of our method.
Starting Page 71
Ending Page 78
Page Count 8
File Format PDF
ISBN 9781450309288
DOI 10.1145/2025876.2025891
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-09-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Data publication Trajectory k-anonymity Privacy-preserving
Content Type Text
Resource Type Article
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