Comprehensive survey on privacy-preserving spatial data query in transportation systems

• Main Authors: MIAO, Yinbin, YANG, Yutao, LI, Xinghua, MENG, Xiangdong, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Spatial data; privacy leakage; location and keywords; spatial data query; Information Security; Numerical Analysis and Scientific Computing; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/8185
• Institution: Singapore Management University

With the rapid development of Intelligent Transportation System (ITS), a large number of spatial data are generated in ITS. Although outsourcing spatial data to the cloud server can reduce the high local computation and storage overheads, it will also lead to security and privacy issues. Therefore, it is necessary to have a survey to specifically summarize these advanced privacy-preserving spatial data query schemes. However, the existing surveys considering both location information and keywords of spatial data only summarize the spatial keyword query scheme in plaintext environment, they do not consider the privacy of spatial data. Although there are some surveys on privacy-preserving spatial data query, they only focus on the location information of spatial data without considering descriptive keywords. Therefore, to understand the progress and research trends in the field, we give a comprehensive survey on secure spatial data query in ITS to summarize and analyze the most advanced solutions. Then, we make a comprehensive and detailed comparison of existing solutions in terms of query function, index structure, time complexity, security, etc. Finally, we show some open challenges and potential research directions for privacy-preserving spatial data query.