Privacy-preserving Location Publishing under Road-network Constraints
Abstract
We are experiencing the expanding use of location-Based services such as AT&T TeleNav GPS Navigator and Intel's Thing Finder. Existing location-Based services have collected a large amount of location data, which have great potential for statistical usage in applications like traffic flow analysis, infrastructure planning and advertisement dissemination. the key challenge is how to wisely use the data without violating each user's location privacy concerns. in this paper, we first identify a new privacy problem, namely inference route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. the experimental results have shown that our approach outperforms the latest related work in terms of both efficiency and effectiveness. © Springer-Verlag Berlin Heidelberg 2010.
Recommended Citation
D. Lin et al., "Privacy-preserving Location Publishing under Road-network Constraints," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5982 LNCS, no. PART 2, pp. 17 - 31, Springer, Dec 2010.
The definitive version is available at https://doi.org/10.1007/978-3-642-12098-5_2
Department(s)
Computer Science
Second Department
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-364212097-8
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
28 Dec 2010
