Privacy-Preserving Travel Recommendation Based on Stay Points Over Outsourced Spatio-Temporal Data

With the pervasiveness of GPS-enabled devices, mobile users can directly visit the best travel routes matching their interests and obtain a better user experience via location-based travel recommendation services. As the number of queries grows, the travel agency for location-based travel recommendations tends to outsource its recommendation services to the cloud server. Since the travel agency’s popular travel routes and raw trajectory data from mobile users contain sensitive information, privacy protection should be guaranteed. Although some schemes have been proposed to solve the privacy problems, no previous works related to the location-based recommendation are proposed over mobile users’ raw trajectories. To solve this problem, the authors propose a privacy-preserving travel recommendation scheme based on stay points over the raw encrypted trajectory data. Specifically, the authors first propose an adapted longest common subsequence computation algorithm to measure the similarity of two trajectories. Second, to support some computations under ciphertext, the authors design several secure two-party computation (S2PC) primitives (e.g., secure division, secure mean coordinate, and secure comparison) based on the Paillier cryptosystem. Third, the authors implement secure stay points extraction and adapted longest common subsequence computation protocols via these secure computation primitives. Finally, the authors analyze the security of the proposed scheme in the semi-honest model and show that the privacy of mobile users’ trajectories, query results, and the travel agency’s popular travel routes are well protected. Meanwhile, the authors evaluate the performance of each secure computation primitive and conduct extensive experiments on synthetic datasets, and the experimental results show that the scheme is practical in the real applications.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3432029
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Han, Liange
  • 0009-0002-5042-0321
  • Luo, W
  • Lu, R
  • Zheng, Yanwei
  • 0000-0002-2115-6237
  • Yang, A
  • Lai, Jintao
  • 0000-0002-6069-6519
  • Cheng, Y
  • Zhang, Yan
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 12999-13013
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Cloud computing; Data analysis; Data privacy; Location; Mobile communication systems; Route choice; Travel behavior
• Subject Areas: Safety and Human Factors; Transportation (General);

Filing Info

• Accession Number: 01942269
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 13 2025 10:32AM