ORCID Profile
0000-0001-8698-2946
Current Organisation
Deakin University
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Publisher: Wiley
Date: 05-07-2020
DOI: 10.1002/CPE.5435
Abstract: In iduals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.
Publisher: Springer Science and Business Media LLC
Date: 29-07-2020
Publisher: Elsevier BV
Date: 04-2020
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 14-04-2020
DOI: 10.1002/CPE.5751
Publisher: Elsevier BV
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: World Scientific Pub Co Pte Ltd
Date: 18-02-2021
DOI: 10.1142/S0219622021410017
Abstract: Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as Outlier-Tree, are not accurate to find out the trend of abnormal traffic for two reasons. First, they discover the propagation pattern based on the detected traffic anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal outlier neighborhoods rather than considering from a global perspective, and thus cannot form a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using the mesh data and enhanced traffic change peaks (en-TCP) to visualize the change of traffic anomalies (e.g., an area where vehicles are gathering or evacuating) and thus accurately capture traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time bin can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level ([Formula: see text]) of pixels. An enhanced adaptive filter is developed to generate traffic change graph sequences from grid traffic images in consecutive periods, and clustering en-TCP in a continuous period is to discover the propagation of traffic anomalies. The accuracy and effectiveness of the proposed method have been demonstrated using a real-world GPS trajectory dataset.
No related grants have been discovered for Guangli HUANG.