Efficient Encrypted Trajectory Similarity Query Over Mobile E-Health Cloud
Xin Wang, Yinbin Miao, Shu Zhang, Qingming Li, Jiawei Zhang, Kefeng Ding, Shouling Ji · IEEE Internet of Things Journal · 2024
Mobile electronic health systems collect a large amount of people’s trajectory data through smart devices (e.g., sensors). Generally, to provide data confidentiality, trajectories are encrypted before being uploaded to cloud servers. Trajectory similarity query has gained widespread attention as a mean to control the spread of infectious diseases. Nonetheless, existing solutions often suffer from low query efficiency and access pattern exposure. To solve these issues, we propose an efficient encrypted trajectory similarity query$(\textsf {TraSQ})$for mobile e-health systems. First, we utilize XZ* index and bijective function to generate the unique trajectory encoding value, thereby reducing storage and query costs over large-scale trajectory datasets. Then, we employ a dual cloud model, secure computing protocols and obfuscation technique to conceal the true trajectory similarity from cloud servers, protecting access pattern. Finally, we formally prove that our scheme achieves privacy protection against chosen plaintext attack (CPA), and conduct extensive experiments to demonstrate that our scheme improves the query efficiency by at least 25.4% when compared with existing solutions.