Visible to the public Adding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization

TitleAdding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization
Publication TypeConference Paper
Year of Publication2020
AuthorsTojiboev, R., Lee, W., Lee, C. C.
Conference Name2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
Date PublishedFeb. 2020
ISBN Number978-1-7281-6034-4
Keywords-Privacy-Publishing-Data, -Surrogate-Vector, big data privacy, business purpose, Cancer, data privacy, data publishing, data utility, Databases, grid computing, Human Behavior, Keywords-—-Noise-trajectory, Metrics, noise trajectory, privacy, privacy protection, pubcrawl, Publishing, resilience, Resiliency, Scalability, scientific research, Trajectory, vector-based grid environment

Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory 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 a strong privacy protection is required, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment.

Citation Keytojiboev_adding_2020