Pystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top-$k$ Spatial–Textual Query
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
The convergence of technologies like Cloud computing, mobile, and smart phone technologies has led to the rapid development of location-based services (LBS) in smart cities. For flexibility and cost savings, there is a recent trend to migrate LBS to the Cloud, however it poses a serious threat to the user privacy. In this paper, we present a new privacy preserving top-k spatio-textual keyword (TkSK) query scheme, called privacy-preserving spatio-textual index (Pystin), which is performed over outsourced Cloud and can enable secure LBS in smart cities. In Pystin, a query user's accurate location is protected by the combination of Boneh-Goh-Nissim homomorphic encryption and hash bucket techniques, and the privacy of textual information are persevered by a one-way hash function. In addition, a quad-tree-based spatio-textual indexing is integrated into Pystin to further reduce the query latency. Detailed security analyzes show that the proposed Pystin scheme is indeed a privacy-preserving TkSK query scheme. Furthermore, extensive experiments are conducted, and results confirm the scalability, efficiency properties of our proposed Pystin scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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