Toward Privacy-Preserving Aggregate Reverse Skyline Query With Strong Security
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
It has been witnessed that Aggregate Reverse Skyline (ARS) query has recently received a wide range of practical applications due to its marvelous property of identifying the influence of query requests. Nevertheless, the query users may hesitate to participate in such query services as the query requests and query results may leak sensitive personal data or valuable business data assets to the service providers. To tackle the concerns, a promising solution is to encrypt the query requests, conduct the ARS queries over encrypted query requests without decrypting, and return the encrypted query results. Unfortunately, many existing solutions are either deployed over a two-server model or unable to fully preserve query privacy. In this paper, we propose a novel privacy-preserving aggregate reverse skyline query (PPARS) scheme on a single server model while ensuring full query privacy. Specifically, we first transform the problem of ARS query into a combination of set membership test and logical expressions. Then, by employing the prefix encoding technique, bloom filter technique, and fully homomorphic encryption, we run the transformed logical expressions to obtain the encrypted aggregate values without leaking query requests, query results, and access patterns. Furthermore, we propose an interpolation-based packing technique to improve the communication efficiency of PPARS. Detailed and formal security analysis demonstrates that our proposed schemes can guarantee strong security. In addition, extensive experiments are conducted, and the results validate the efficiency of our proposed schemes.
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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 | 0.000 | 0.003 |
| Open science | 0.001 | 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