2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 21 references
TL;DR
Results demonstrate that the critical role of structured text preparation and semantic representation in enhancing model reliability, interpretability, and scalability for automated vulnerability prioritization, and underscore the critical role of structured text preparation and semantic representation in enhancing model reliability, interpretability, and scalability for automated vulnerability prioritization.
Abstract
This study introduces a privacy-preserving data preparation and feature engineering framework designed for machine learning-based cybersecurity vulnerability risk prediction, utilizing real-world enterprise scan data from an operational cloud environment. To transform raw vulnerability records into a structured, machine-readable format while complying with personal data protection regulations, the framework integrates systematic data cleaning, missing value imputation, categorical encoding, text normalization, and host pseudonymization. Exploratory data analysis (EDA) was conducted on 36,940 operational records to examine dataset characteristics, vulnerability distributions, and severe class imbalances. Multiple classifiers—including Logistic Regression, Random Forest, XGBoost, and LightGBM—were evaluated under extreme imbalance conditions mitigated via SMOTE. Experimental results demonstrate that non-linear tree-based ensembles achieved the highest predictive performance, with LightGBM attaining a macro F1-score of 0.9974. Comparative analysis further indicates that the effectiveness of textual representations depends on the underlying classifier architecture, with semantic embeddings yielding the best performance when combined with tree-based ensemble models, while TF-IDF remained competitive for linear classification. These findings underscore the critical role of structured text preparation and semantic representation in enhancing model reliability, interpretability, and scalability for automated vulnerability prioritization.
A federated deep learning framework that systematically integrates adaptive privacy noise mechanisms and trust-weighted aggregation within a distributed architecture that ensures the protection of sensitive data during collaborative analysis through precise differential privacy control and advanced neural network models is presented.
The results demonstrate that federated learning is a scalable and effective method that can achieve privacy compliance in e-commerce analytics within data-restricted environments, and it lays a solid foundation for secure distributed business intelligence.
Jing Hao· International Conference on...· 0 citations
Government electronic payment gateways concentrate public revenue and citizen data, making them high-value targets whose compromise carries national-security consequences. Yet vulnerability assessment in many public institutions remains manual, severity-driven, and unable to scale against both the growing volume of disclosed weaknesses and the continuous stream of live attack traffic. To address both vulnerability surfaces within a single coherent assessment layer, this paper presents a dual-module machine learning framework that unifies static prioritisation of disclosed vulnerabilities with runtime detection of malicious traffic. Methodologically, the static module prioritises disclosed vulnerabilities by pairing Term Frequency–Inverse Document Frequency (TF-IDF) text representation with Bayesian-optimised XGBoost multi-class classification over a corpus of 386,337 records, built by expanding National Vulnerability Database (NVD) records across their affected libraries using Open Source Vulnerabilities (OSV) data; the runtime module detects malicious HTTP requests using twelve engineered request features and Bayesian-optimised XGBoost binary classification on the CSIC 2010 dataset of 61,065 requests. On the key results, the static module attains a test accuracy of 0.8630, a macro-averaged F1-score of 0.8475, and High-tier recall of 0.9356; the runtime module attains an accuracy of 0.9101, an F1-score of 0.8879, and a ROC-AUC of 0.9796, raising attack recall from 0.087 under a rule-based signature baseline to 0.868. These results demonstrate that interpretable, low-cost learning models can strengthen vulnerability assessment for government payment infrastructure in resource-constrained settings, contributing the first data-driven, reproducible vulnerability-assessment model developed specifically for the Tanzanian Government Electronic Payment Gateway (GePG) context. As principal limitations, we identify the proxy-label limitation, on which the static module is trained against library-exposure breadth rather than a severity or exploitation measure, and the need for institution-specific validation on live GePG traffic, which together define the principal paths for refinement.
Unknown authors· East African Journal of Info...· 0 citations
This paper presents a systematic framework for membership inference attacks, in which an adversary with only black-box query access to a deployed classifier determines whether a specific individual's record was part of its training set.
Pramod Prakash· International Journal of Int...· 0 citations
Abstract Background The development of robust medical AI for knowledge discovery and decision support commonly necessitates large-scale datasets from multiple institutions. However, such data aggregation is severely constrained by privacy regulations and the inherent risk of sensitive information leakage, making it difficult to navigate the utility-privacy trade-off. Objective We aimed to design a secure multiparty deep learning system that enables privacy-preserving modeling from distributed medical time-series data without centralizing raw information or exposing model parameters. Our goal was to achieve predictive accuracy comparable to nonsecure models while providing strong security and efficiency. Methods We developed a framework using threshold homomorphic encryption to securely train recurrent neural networks on distributed longitudinal data. To improve the efficiency, we proposed an optimized encrypted matrix multiplication scheme, a secure ciphertext refresh protocol, and used lightweight encryption parameters and low-degree approximated activation polynomials. The system was evaluated on 4 real-world intensive care unit datasets for tasks like mortality and sepsis prediction. Results The system demonstrated practical efficiency, requiring approximately 1 minute per training iteration for processing 125 local batches over 39 variables and 48 time steps, and scaling well with data size and participant number. Securely trained models achieved predictive performance that was comparable to, and in some cases superior to, nonsecure centralized models, highlighting their ability to learn generalizable patterns in different unseen data distributions. For example, on the PhysioNet Challenge 2012 dataset, our secure model achieved an area under the curve (AUC) of 0.8480, outperforming the nonsecure baseline AUC of 0.8404. Conclusions This work provides a viable and efficient solution for cross-institutional, privacy-preserving analysis of longitudinal medical data. The framework successfully bridges the utility-privacy gap, facilitating safer collaborative research and enabling robust knowledge discovery and decision support while adhering to strict data protection standards.
Yao Lu, Yu Tian, Tianshu Zhou et al.· JMIR Formative Research· 0 citations
Now, AI runs on cloud platforms, edge systems with federated settings, and in large language model (LLM) pipelines or data-sharing services, creating even wider privacy leakage paths beyond classical database disclosure. This paper offers a systematic, structured review of the literature on a curated, cost-effective reference corpus for quantifying and preventing privacy leakage in AI-enabled data ecosystems. The review ties together four strands of research that are often treated separately. Firstly, the privacy risk throughout the AI life cycle. Secondly, the measurement of the quantitative leakage. Thirdly, architectures of the privacy-preserving models, and finally, operational governance for real-world deployment. Our analysis demonstrates that state-of-the-art approaches are moving from static mechanisms based on anonymization to metric-aware protections, including information-theoretic leakage scores, cumulative differential privacy accounting, personalized privacy budgets, and benchmark-driven attack evaluation. In parallel, prevention methods are evolving beyond single homomorphic noise injection and are becoming multi-layered defenses that combine differential privacy, federated learning, weight quantization, synthetic data generation, policy-driven automation, and LLM controls. The review uncovers four itchy gaps: fractured assessment metrics, shaky privacy-utility trade-offs, flimsy integration of technological controls and compliance processes, and low cross-context validation across cloud-based computing, edge computing (data processing at or near the source), federated learning (distributed machine-learning methods), and generative AI systems. The paper concludes by outlining a unified research agenda to build AI-aware, quantifiable, and usable privacy protection stacks.
