Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection
Bibliographic record
Abstract
In the era of big data, clustering and data mining have become essential tools for uncovering patterns and insights from vast datasets. However, these processes often involve the use of sensitive data, raising significant concerns about privacy, security, and trustworthiness. This paper proposes N2P-CM, a novel privacy-preserving framework designed to protect sensitive information during the entire clustering and mining lifecycle. Unlike existing methods that focus on partial aspects of security or apply generic encryption techniques, N2P-CM integrates five innovative and synergistic modules: Sensitive Feature Obfuscation, Adaptive Trust Weight Aggregation, Compressed Secure Semantic Embedding, Differential Traceable Execution Engine, and Blockchain Auditable Ledger. Each module contributes a distinct layer of privacy and accountability, ranging from feature-level data transformation and federated trust scoring to secure semantic encoding and traceable execution logging with blockchain support. We provide formal definitions and algorithms for each module and demonstrate their integration in a unified architecture. Extensive simulations using real-world datasets validate the efficacy of N2P-CM, showing that it achieves strong privacy guarantees with minimal degradation in clustering accuracy. This research contributes a comprehensive and modular solution to the growing need for privacy-preserving analytics in sensitive domains such as healthcare, finance, and smart cities.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".