SECURE MANAGEMENT AND DISSEMINATION OF BIG DATA IN CLOUD COMPUTING ENVIRONMENTS

Abstract

This research develops a strong framework that implements cloud computing technologies to address the fast-growing big data environment and its ensuing data management difficulties and analytical requirements and compression needs and security threats. The solution implements a multi-tiered big data workflow security model that balances flexibility with user-friendliness and aims to maximize both performance and security. The “Futuristic Data Processing Tool” serves as a demonstration of this framework through its implementation based on Python and Tkinter within a desktop application. The tool merges essential functions that let users acquire data followed by statistical inspections and visual presentations and indexing and compression testing and encryption capabilities.

The system leverages Random Forest machine learning for data classification followed by a dictionary compression algorithm and implements an encryption methodology mixing traditional and asymmetric along with symmetric cryptography. The algorithm achieves effective data compression according to experimental outcomes which demonstrate a significant 63.20% compression ratio. The multi-tier encryption method together with traditional symmetric keys and asymmetric schemes provides strong data protection mainly when working with unsecure cloud platforms. The application's user-friendly interface enables users of all experience levels to manage complex data through CSV and SQLite file connections.

The research established that the framework delivers practicality alongside security features for creating scalable cloud data solutions of the future.