Privacy-Preserving Travel Recommendation Based on Stay Points Over Outsourced Spatio-Temporal Data
Bibliographic record
Abstract
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, we propose a privacy-preserving travel recommendation scheme based on stay points over the raw encrypted trajectory data. Specifically, we first propose an adapted longest common subsequence computation algorithm to measure the similarity of two trajectories. Second, to support some computations under ciphertext, we design several secure two-party computation (S2PC) primitives (e.g., secure division, secure mean coordinate, and secure comparison) based on the Paillier cryptosystem. Third, we implement secure stay points extraction and adapted longest common subsequence computation protocols via these secure computation primitives. Finally, we analyze the security of our 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, we evaluate the performance of each secure computation primitive and conduct extensive experiments on synthetic datasets, and the experimental results show that our scheme is practical in the real applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".