WeightedSim: privacy-preserving weighted similarity query over encrypted healthcare data
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 development of smart applications in e-healthcare has aroused the exponential growth of healthcare data.Therefore, the data owner tends to outsource them to powerful cloud servers, which can provide query services.However, for privacy concerns, the data owner may outsource the encrypted data instead of plaintexts.Moreover, when using the data query service, query users may search the data based on their preferences.To address the aforementioned issues, in this paper, we propose WeightedSim, an efficient and privacy-preserving weighted similarity range query scheme for outsourced healthcare data.Specifically, we first develop an encrypted R-tree index by utilising the symmetric homomorphic encryption (SHE) technique and then employ it to perform a weighted similarity range query under the two cloud servers model.We analyse the security of our scheme to be selectively secure when the SHE is semantically secure against CPA and also conduct extensive experiments to validate the scheme's efficacy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.073 | 0.070 |
| 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