Accurate, Secure, and Efficient Semi-Constrained Navigation Over Encrypted City Maps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Navigation services enable users to find the shortest path from a starting point <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> to a destination <inline-formula><tex-math notation="LaTeX">$D$</tex-math></inline-formula>, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$D$</tex-math></inline-formula> while passing through <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> <i>unordered stops</i>, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermes to achieve accurate, secure, and efficient SCN. Specifically, we propose a divide-and-conquer approach to strike a good balance between accuracy and efficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three sequential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than <inline-formula><tex-math notation="LaTeX">$O(k^{2})$</tex-math></inline-formula>. We experiment over a real-world city map and compare with six baselines. Results show that path search with <inline-formula><tex-math notation="LaTeX">$k=4$</tex-math></inline-formula> among <inline-formula><tex-math notation="LaTeX">$N=1000$</tex-math></inline-formula> intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it