Preliminary Study about Advantageous Trajectory Anonymization methods Based on Real Trajectory data

2017 Fiscal Year Final Research Report

• Project/Area Number: 16K12548
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Library and information science/Humanistic social informatics
• Research Institution: The University of Tokyo
• Principal Investigator: Yamaguchi Rie (繁富利恵) 東京大学, 大学院情報理工学系研究科, 特任准教授 (90443192)
• Co-Investigator(Kenkyū-buntansha): 中川 裕志 東京大学, 情報基盤センター, 教授 (20134893)
• Project Period (FY): 2016-04-01 – 2018-03-31
• Keywords: プライバシー保護 / 情報セキュリティ / 位置情報 / 匿名化

Outline of Final Research Achievements

Growing mobile networks and widely spread of Global Positing System (GPS) devices enables to collect large scale location and trajectory data. Using trajectory data to succeed, privacy and data characteristics are essential. Most anonymization methods are losing characteristic for service providers, like adding noise to trajectories.
In this research, firstly we present adaptive quadtree grid population calculation to determine grid size of trajectories. Our anonymization method dynamically adjust cluster size to maintain trajectory data characteristics, small mesh to the dense populated area, large mesh to sparse populated area, base on quadtree geospatial data structure. Proposed method is satisfied that adaptive size scaling and more efficient to maintain characteristic. Our experiment suggest that proposed method correctly anonymize Tokyo Urban flow data of 1.3M Trajectories.

Free Research Field

情報セキュリティ