STDP: Secure Privacy-Preserving Trajectory Data Publishing

As the smart devices and cloud services are rapidly expanding, a large amount of location information can easily be gathered. However, there is a conflict between collecting location information and protecting personal information since obtaining and utilizing the information may be restricted due to privacy concerns. In fact, various methods which use K-anonymity for original location data have been studied, but these methods have excessively reduced data utility while stressing highly on privacy preservation. In this research, we suggest a novel model to overcome this fundamental dilemma. Compared to the existing approaches, our study shows a new theoretical advancement in privacy protection and outstanding performance in terms of time complexity and data utility.