Earticle

초록

영어

Trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory it without proper privacy policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness, have been studied, though they tend to protect by reducing data depends on a feature of each method. When it requires strong privacy protection, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, for the first time, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment. Afterward, we propose an efficient algorithm to expand trajectories adding the minimum amount of noise. Compared to other methods, our experiment shows the proposed algorithm maintains excellent performance with respect to data utility and time complexity.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
3. PRIVACY ANONYMIZATION ALGORITHM
4. CONCLUSIONS
REFERENCES

키워드

저자

• Rashid Tojiboev [ Dept. of Industrial Engineering Inha University Incheon, South Korea ]
• Chris Soo-Hyun Eom [ Dept. of Industrial Engineering Inha University Incheon, South Korea ]
• Wookey Lee [ Dept. of Industrial Engineering Inha University Incheon, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국EA학회 학술발표논문집

• 간기

  연간

• 수록기간

  2004~2024

• 십진분류

  KDC 325 DDC 658