IPP-DMS: A scalable privacy-preserving data management system for secure and efficient handling of large-scale datasets

The rapid growth of data in various industries has led to a pressing need for innovative solutions that ensure both the security and efficiency of data management systems. As organizations increasingly rely on large-scale datasets for decision-making, ensuring the privacy and integrity of this data becomes a critical challenge. The study introduces the Integrated Privacy-Preserving Data Management System (IPP-DMS), designed to enhance data security, computational efficiency, and user privacy. Unlike conventional systems, IPP-DMS focuses on processing large-scale datasets, such as financial records, customer interactions, and sensor data, employing advanced feature extraction techniques like Gray-Level Co-occurrence Matrix (GLCM) and Principal Component Analysis (PCA) to uncover critical patterns and optimize data handling. The system uses Advanced Encryption Standard (AES) for robust data encryption, differential privacy for data anonymization, and Role-Based Access Control (RBAC) for secure user management. Additionally, an AI-driven anomaly detection module monitors real-time access patterns to ensure compliance with data protection regulations. With an AUC score of 0.91 for anomaly detection, a 0.85-second authentication time, and a 0.45-second access delay, experimental data demonstrate that IPP-DMS works better than conventional systems. In addition, the system provides better scalability and efficiency than PACS, SEISS, and DEIS, processing up to 2000 requests per minute. IPP-DMS provides a scalable, secure, and efficient solution for privacy-preserving data management, setting a new benchmark in the field by integrating advanced methodologies with standard security measures.