Privacy-Preserving Data Protection: A Novel Mechanism for Maximizing Availability without Compromising Confidentiality

Abstract

With the advent of big data and cloud computing, maintaining data availability while keeping it confidential is a major issue. Conventional encryption-based techniques are prone to introducing computational latency and affecting real-time data access. To mitigate this, we introduce a Concatenated Deep Belief Network with Random Cray Dimensional Optimization (CDBN-RCDO) as an intelligent privacy-preserving data protection mechanism. The CDBN structure effectively derives hierarchical feature representations, supporting strong anomaly detection and access control, while the RCDO algorithm optimizes feature selection and dimensionality reduction, supporting both security and system efficiency. Our model maximizes data availability by dynamically balancing computational complexity and encryption strength, minimizing access latency without sacrificing confidentiality. Experimental tests show that the given technique performs better compared to traditional privacy-preserving methods in data retrieval speed, security strength, and scalability in heterogeneous data environments. The study presents a new approach toward secure high-availability data handling within cloud and IoT-based ecosystems.