Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lack quantitative granularity or fail to adapt to dynamic, heterogeneous data environments. In this work, we propose a unified two-phase linkability quantification framework that systematically measures privacy risks at both the inter-dataset and intra-dataset levels. Our approach integrates unsupervised clustering on attribute distributions with record-level matching to compute interpretable, fine-grained risk scores. By aligning risk measurement with regulatory standards such as the GDPR, our framework provides a practical, scalable solution for safeguarding user privacy in evolving data-sharing ecosystems. Extensive experiments on real-world and synthetic datasets show that our method achieves up to 96.7% precision in identifying true linkage risks, outperforming the compared baseline by 13 percentage points under identical experimental settings. Ablation studies further demonstrate that the hierarchical risk fusion strategy improves sensitivity to latent vulnerabilities, providing more actionable insights than previous privacy gain-based metrics.

Fault diagnosis techniques are essential for preventing equipment failures, reducing maintenance costs, and enhancing operational efficiency by promptly identifying anomalies. The widespread deployment of industrial sensors has significantly increased the availability of machinery data, facilitating extensive research in data-driven fault diagnosis. However, real-world datasets frequently exhibit label scarcity and severe class imbalance, where fault instances are substantially fewer than normal samples. To address these challenges, this study proposes a robust unsupervised domain adaptation framework that synthesizes fault signals by interpolating real healthy samples with domain-specific knowledge. Although this synthetic augmentation effectively expands training data, the resulting distribution often deviates from actual fault scenarios, limiting model generalizability. To alleviate this domain discrepancy, our framework incorporates Conditional Domain-Adversarial Networks (CDAN) for domain-invariant feature extraction, complemented by structured pseudo-labeling to assign reliable predictions to unlabeled target samples. Subsequently, a Locality Preserving Projection (LPP) module constructs a shared latent space to achieve both domain alignment and enhanced class discrimination. Experimental evaluations conducted on a synthetic dataset derived from the CWRU bearing benchmark demonstrate that the proposed method achieves accuracies of 91.10% under imbalanced conditions and 84.65% in balanced scenarios, surpassing current state-of-the-art methods by 12.87% and 3.57%, respectively. Ablation studies further underscore the significant contribution of structured pseudo-labeling to the overall performance, confirming the proposed approach’s efficacy and robustness in real-world unsupervised industrial fault diagnosis tasks.

Cross-material continuous tool wear prediction is difficult because a model must preserve the physical wear scale, not only align high-dimensional sensor features. This limitation is critical in milling, where the target variable is the continuous flank wear width ($\mathrm{VB}$) and material shift can distort the mapping from sensor response to wear magnitude. We address this problem by recasting cross-domain tool wear prediction as monotone wear-scale adaptation. We propose Multi-Physics Monotone Score Transport (MPMST), a monotone score transport framework that constructs a tool-wear-oriented score from sensor-derived candidate cues, transports the target-domain score onto the source-domain wear scale, and then predicts wear through isotonic regression. We also evaluate One-Physics Monotone Score Transport (OPMST), a force-only variant that uses the same score-transport pipeline with a restricted cue family. On Mondragon Unibertsitatea–Tool Condition Monitoring (MU-TCM) with two cross-material transfer tasks, the validation-driven MPMST configuration reduces mean absolute error by approximately 63% relative to Correlation Alignment (CORAL) and by approximately 31% relative to a physics-informed Gaussian process baseline. The results support monotone score construction and score transport as practical mechanisms for continuous tool wear prediction under domain shift, while also showing that MU-TCM is strongly force dominated.