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Trajectory Privacy for Publishing Big Data
Rashid Tojiboev,Chris Soo-Hyun Eom,Wookey Lee 한국EA학회 2019 한국EA학회 학술발표논문집 Vol.2019 No.10
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.