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Open AccessArticle

A Trajectory Privacy Preserving Scheme in the CANNQ Service for IoT

by Lin Zhang 1,2,3,*, Chao Jin 1, Hai-ping Huang 1,2,3, Xiong Fu 1,2,3 and Ru-chuan Wang 1,2,3
1
School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
3
Institute of Computer Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2190; https://doi.org/10.3390/s19092190
Received: 28 February 2019 / Revised: 8 May 2019 / Accepted: 9 May 2019 / Published: 12 May 2019
(This article belongs to the Special Issue Security and Privacy in Internet of Things)
Nowadays, anyone carrying a mobile device can enjoy the various location-based services provided by the Internet of Things (IoT). ‘Aggregate nearest neighbor query’ is a new type of location-based query which asks the question, ‘what is the best location for a given group of people to gather?’ There are numerous, promising applications for this type of query, but it needs to be done in a secure and private way. Therefore, a trajectory privacy-preserving scheme, based on a trusted anonymous server (TAS) is proposed. Specifically, in the snapshot queries, the TAS generates a group request that satisfies the spatial K-anonymity for the group of users—to prevent the location-based service provider (LSP) from an inference attack—and in continuous queries, the TAS determines whether the group request needs to be resent by detecting whether the users will leave their secure areas, so as to reduce the probability that the LSP reconstructs the users’ real trajectories. Furthermore, an aggregate nearest neighbor query algorithm based on strategy optimization, is adopted, to minimize the overhead of the LSP. The response speed of the results is improved by narrowing the search scope of the points of interest (POIs) and speeding up the prune of the non-nearest neighbors. The security analysis and simulation results demonstrated that our proposed scheme could protect the users’ location and trajectory privacy, and the response speed and communication overhead of the service, were superior to other peer algorithms, both in the snapshot and continuous queries. View Full-Text
Keywords: aggregate nearest neighbor query; trajectory privacy; spatial K-anonymity; secure areas; strategy optimization aggregate nearest neighbor query; trajectory privacy; spatial K-anonymity; secure areas; strategy optimization
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Zhang, L.; Jin, C.; Huang, H.-P.; Fu, X.; Wang, R.-C. A Trajectory Privacy Preserving Scheme in the CANNQ Service for IoT. Sensors 2019, 19, 2190.

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