MELT: Mapreduce-Based Efficient Large-Scale Trajectory Anonymization
With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, they will be valuable assets to various service providers to explore business opportunities, to governments to research commuter behavior for better transport management, and could also greatly benefit the general public for day to day commute. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy as users' trajectories may be tracked by an adversary to discover sensitive information, such as home locations, their children's school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns, none of these prior works address the scalability issue since it is a newly occurring problem. In this paper, we conquer these two challenges by designing a novel trajectory anonymization algorithm using the MapReduce programming paradigm to provide scalability, strong privacy protection and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the latest centralized approaches.
K. Ward et al., "MELT: Mapreduce-Based Efficient Large-Scale Trajectory Anonymization," Proceedings of the 29th International Conference on Scientific and Statistical Database Management (2017, Chicago, IL), Association for Computing Machinery (ACM), Jun 2017.
The definitive version is available at https://doi.org/10.1145/3085504.3085581
29th International Conference on Scientific and Statistical Database Management, SSDBM '17 (2017: Jun. 27-29, Chicago, IL)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Behavioral research; Location; Management information systems; Scalability; Trajectories; Business opportunities; Centralized approaches; Effectiveness and efficiencies; Individual privacy; Map-reduce programming; Privacy protection; Sensitive informations; Transport management; Location based services
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2017 Association for Computing Machinery (ACM), All rights reserved.
01 Jun 2017
This work was funded by the National Science Foundation (NSFDGE-1433659, NSF-IIP-1332002), Department of Education (P200A120110).