Novel trajectory privacy-preserving method based on clustering using differential privacy-Reference-Cited by-同舟云学术

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Novel trajectory privacy-preserving method based on clustering using differential privacy

Published:2020-07 Issue: Volume:149 Page:113241
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Zhao Xiaodong,Pi Dechang,Chen Junfu

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Foundation of Graduate Innovation Center in NUAA

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference40 articles.

1. Geo-indistinguishability: Differential privacy for location-based systems;Andrés,2013

2. Practical privacy: Thesulq framework[C];Blum,2005

3. Trajectory anonymization: Balancing usefulness about position information and timestamp[c];Chiba,2019

4. Clustering geo-indistinguishability for privacy of continuous location traces[c];Cunha,2019

5. PLDP-TD: Personalized-location differentially private data analysis on trajectory databases;Deldar;Pervasive and Mobile Computing,2018

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