PPTPF: Privacy-Preserving Trajectory Publication Framework for CDR Mobile Trajectories

ISPRS International Journal of Geo-Information

SinG. Teo¹  Manoranjan Dash²  Jianxi Yang³ 

[1] Cybersecurity Department, Institute for Infocomm Research, Singapore 138632, Singapore;Data Science Consortium, National University of Singapore, Singapore 117602, Singapore;School of Information Science and Engineering, Chongqing Jiaotong University, Nanán District, Chongqing 400074, China;

关键词: spatio-temporal data;    privacy-preserving;    utility;    k-anonymization;   

DOI  :  10.3390/ijgi10040224

来源: DOAJ

【 摘 要 】

As mobile phone technology evolves quickly, people could use mobile phones to conduct business, watch entertainment shows, order food, and many more. These location-based services (LBS) require users’ mobility data (trajectories) in order to provide many useful services. Latent patterns and behavior that are hidden in trajectory data should be extracted and analyzed to improve location-based services including routing, recommendation, urban planning, traffic control, etc. While LBSs offer relevant information to mobile users based on their locations, revealing such areas can pose user privacy violation problems. An efficient privacy preservation algorithm for trajectory data must have two characteristics: utility and privacy, i.e., the anonymized trajectories must have sufficient utility for the LBSs to carry out their services, and privacy must be intact without any compromise. Literature on this topic shows many methods catering to trajectories based on GPS data. In this paper, we propose a privacy preserving method for trajectory data based on Call Detail Record (CDR) information. This is useful as a vast number of people, particularly in underdeveloped and developing places, either do not have GPS-enabled phones or do not use them. We propose a novel framework called Privacy-Preserving Trajectory Publication Framework for CDR (PPTPF) for moving object trajectories to address these concerns. Salient features of PPTPF include: (a) a novel stay-region based anonymization technique that caters to important locations of a user; (b) it is based on Spark, thus it can process and anonymize a significant volume of trajectory data successfully and efficiently without affecting LBSs operations; (c) it is a component-based architecture where each component can be easily extended and modified by different parties.

【 授权许可】

Unknown