Achieving Practical and Privacy-Preserving kNN Query Over Encrypted Data
Bibliographic record
Abstract
As one of the most popular queries in big data era, the$k$nearest neighbors ($k$NN) query plays a significant role in various applications, such as medical diagnosis, signal processing, and recommendation systems. Meanwhile, driven by the advancement of the cloud service, an emerging trend among applications is to outsource the dataset and the corresponding$k$NN query services to the cloud. However, as the cloud is not fully trusted, those applications will face vital privacy concerns, and thus they usually encrypt data before outsourcing them to the cloud. Because encrypted data are outsourced to cloud, the$k$NN query over encrypted data has become increasingly attractive, and many solutions have been put forth in recent years. However, existing solutions cannot fully satisfy the objects of returning exact query results, protecting database privacy and query privacy, achieving high query efficiency, and imposing low computational costs at the user side. To address these issues, in this paper, we propose a new practical and privacy-preserving$k$NN query scheme. Specifically, we first refine the general security requirements for the matrix encryption by systematically analyzing existing algorithms. Then, we design a novel asymmetric matrix encryption (AME) to securely achieve Euclidean distance computation and two distances comparison in a single-party and non-interactive way. Then, based on the AME scheme, we propose a privacy-preserving$k$NN query scheme, in which a max-heap of size$k$is used to accelerate query efficiency. Detailed security analysis shows that our proposed scheme is really privacy-preserving. In addition, extensive performance evaluations are conducted, and the results demonstrate that our proposed scheme is also highly efficient.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".