Privacy Preserving Scheme for Location-Based Services
Bibliographic record
Abstract
Homomorphic encryption schemes make it possible to perform arithmetic operations, like additions and multiplications, over encrypted values. This capability provides enhanced protection for data and offers new research directions, including blind data processing. Using homomorphic encryption schemes, a Location-Based Service (LBS) can process encrypted inputs to retrieve encrypted location-related information. The retrieved encrypted data can only be decrypted by the user who requested the data. The technology still faces two main challenges: the encountered processing time and the upper limit imposed on the allowed number of operations. However, the protection of users’ privacy achieved through this technology makes it attractive for more research and enhancing. In this paper we use homomorphic encryption schemes to build a fully secure system that allows users to benefit from location-based services while preserving the confidentiality and integrity of their data. Our novel system consists of search circuits that allow an executor (i.e. LBS server) to receive encrypted inputs/requests and then perform a blind search to retrieve encrypted records that match the selection criterion. A querier can send the user’s position and the service type he/she is looking for, in encrypted form, to a server and then the server would respond to the request without any knowledge of the contents of the request and the retrieved records. We further propose a prototype that improves the practicality of our system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".