Precise sensitivity recognizing, privacy preserving, knowledge graph-based method for trajectory data publication

2021 ◽  
Vol 16 (4) ◽  
Author(s):  
Xianxian Li ◽  
Bing Cai ◽  
Li-e Wang ◽  
Lei Lei
Keyword(s):  
Privacy Preserving ◽  
Knowledge Graph ◽  
Trajectory Data ◽  
Data Publication

TPPG: Privacy-preserving trajectory data publication based on 3D-Grid partition

2019 ◽  
Vol 23 (3) ◽  
pp. 503-533 ◽  
Author(s):  
Chuanming Chen ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Guiyin Hu
Keyword(s):  
Privacy Preserving ◽  
Trajectory Data ◽  
Data Publication ◽  
Grid Partition

scholarly journals A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 115717-115727
Author(s):  
Bin Yu ◽  
Chenyu Zhou ◽  
Chen Zhang ◽  
Guodong Wang ◽  
Yiming Fan
Keyword(s):  
Privacy Preserving ◽  
Knowledge Graph ◽  
Task Framework

Personalized Privacy-Preserving Trajectory Data Publishing

2017 ◽  
Vol 26 (2) ◽  
pp. 285-291 ◽  
Author(s):  
Qiwei Lu ◽  
Caimei Wang ◽  
Yan Xiong ◽  
Huihua Xia ◽  
Wenchao Huang ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Trajectory Data

An efficient method for privacy-preserving trajectory data publishing based on data partitioning

2019 ◽  
Vol 76 (7) ◽  
pp. 5276-5300 ◽  
Author(s):  
Songyuan Li ◽  
Hong Shen ◽  
Yingpeng Sang ◽  
Hui Tian
Keyword(s):  
Efficient Method ◽  
Privacy Preserving ◽  
Data Partitioning ◽  
Data Publishing ◽  
Trajectory Data

scholarly journals A novel privacy preserving method for data publication

2019 ◽  
Vol 501 ◽  
pp. 421-435 ◽  
Author(s):  
Chaobin Liu ◽  
Shixi Chen ◽  
Shuigeng Zhou ◽  
Jihong Guan ◽  
Yao Ma
Keyword(s):  
Privacy Preserving ◽  
Data Publication

scholarly journals Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 207 ◽  
Author(s):  
Elias Dritsas ◽  
Maria Trigka ◽  
Panagiotis Gerolymatos ◽  
Spyros Sioutas 
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Motion Vector ◽  
Research Work ◽  
Privacy Preserving ◽  
Mobile Users ◽  
Trajectory Clustering ◽  
K Nearest Neighbor ◽  
Trajectory Data ◽  
Spatiotemporal Databases

In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data. The k-anonymity set consists of the k nearest neighbors. We constructed a motion vector of the form (x,y,g,v) where x and y are the spatial coordinates, g is the angle direction, and v is the velocity of mobile users, and studied the problem in four-dimensional space. We followed two approaches. The former applied only k-Nearest Neighbor (k-NN) algorithm on the whole dataset, while the latter combined trajectory clustering, based on K-means, with k-NN. Actually, it applied k-NN inside a cluster of mobile users with similar motion pattern (g,v). We defined a metric, called vulnerability, that measures the rate at which k-NNs are varying. This metric varies from 1 k (high robustness) to 1 (low robustness) and represents the probability the real identity of a mobile user being discovered from a potential attacker. The aim of this work was to prove that, with high probability, the above rate tends to a number very close to 1 k in clustering method, which means that the k-anonymity is highly preserved. Through experiments on real spatial datasets, we evaluated the anonymity robustness, the so-called vulnerability, of the proposed method.

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