MAPLE: A Mobile Scalable P2P Nearest Neighbor Query System for Location-based Services

2006 ◽  
Author(s):  
Wei-Shinn Ku ◽  
R. Zimmermann ◽  
Chi-Ngai Wan ◽  
Haojun Wang
Keyword(s):  
Nearest Neighbor ◽  
Location Based Services ◽  
Nearest Neighbor Query ◽  
Query System

scholarly journals A Trajectory Privacy Preserving Scheme in the CANNQ Service for IoT

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2190 ◽  
Author(s):  
Lin Zhang ◽  
Chao Jin ◽  
Hai-ping Huang ◽  
Xiong Fu ◽  
Ru-chuan Wang
Keyword(s):  
Nearest Neighbor ◽  
Security Analysis ◽  
Response Speed ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Continuous Queries ◽  
Communication Overhead ◽  
Nearest Neighbor Query ◽  
Aggregate Nearest Neighbor ◽  
Query Algorithm

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.

scholarly journals Secure and Practical Group Nearest Neighbor Query for Location-Based Services in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jingjing Guo ◽  
Jiacong Sun
Keyword(s):  
Data Structure ◽  
Data Privacy ◽  
High Efficiency ◽  
Nearest Neighbor ◽  
Resource Constraints ◽  
Location Based Services ◽  
Nearest Neighbor Query ◽  
Tree Data ◽  
Random Database ◽  
Tree Data Structure

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from k − anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with n n ≥ 3 LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec GNN + ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec GNN + scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec GNN + schemes. The experimental results show the high efficiency of our proposed schemes.

NNlists-based k-path nearest neighbor query in road networks

2010 ◽  
Vol 30 (7) ◽  
pp. 1947-1949
Author(s):  
Bao-wen WANG ◽  
Jing-jing HAN ◽  
Zi-jun CHEN ◽  
Wen-yuan LIU
Keyword(s):  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Query

MapReduce algorithms for the K group nearest-neighbor query

2019 ◽  
Author(s):  
Panagiotis Moutafis ◽  
Francisco García-García ◽  
George Mavrommatis ◽  
Michael Vassilakopoulos ◽  
Antonio Corral ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Query
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