What question did this study set out to answer?

The central aim is to develop enhanced privacy-preserving data mining methods in healthcare to protect sensitive information while maintaining utility.

February 5, 2026

Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics

Key Points

The central aim is to develop enhanced privacy-preserving data mining methods in healthcare to protect sensitive information while maintaining utility.
Proposed optimized lightweight federated learning framework for improved computational efficiency.
Hybrid blockchain-based approach for secure verifiable transactions among healthcare institutions.
Developed scalable privacy-preserving deep learning model for large patient datasets.
Created a privacy-preserving AI benchmarking framework for evaluating sensitive data compliance with regulations.
The optimized federated framework significantly reduces computational loads and risks of privacy leakage.
Hybrid blockchain approach decreases transaction overhead while ensuring data security.
The proposed deep learning model effectively handles large datasets without compromising privacy.
The benchmarking framework aids in aligning healthcare data practices with HIPAA and GDPR regulations.

Abstract

Their fields have a profound interest in PPDM as a technical progress in health informatics, balancing the need to extract valuable information for clinical decisions while preserving sensitive data. Classic federated learning (FL) models have various limitations like intensive computational loads and privacy leakage risks. In this paper, we propose an optimized lightweight federated framework that increases computational efficiency without compromising privacy properties. Furthermore, an adaptive noise optimiz… (Note: This is the previous version condensed to lower the response time but are still ok.) In addition, because of security, a hybrid blockchain integrated data mining approach is created to implemented secure verifiable transaction with reduced the overhead in multiple health care institutions. In addition, a scalable privacy-preserving deep learning model is proposed for big patient datasets. To address this challenge, this work develops a full-fledged privacy-preserving AI benchmarking framework for the harmonized evaluation of sensitive data across different healthcare data sets. Lastly, the suggested framework helps to identify alignment with global privacy regulations including HIPAA and GDPR, thus enabling ethical compliance and encouraging responsible AI-led healthcare innovations. Our study paves the way for a secure, scalable, and efficient privacy-preserving data mining in the healthcare informatics ecosystem.

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