Implementing Privacy Preserving Techniques in Distributed Big Data Framework on Cloud Computing

Abstract

Cloud-based deployment architectures have emerged as the preferred model for Big Data operations due to their scalability, flexibility, and cost-effectiveness. However, this shift raises new security concerns as data is no longer directly under the user's control. Securing Big Data in cloud environments is crucial for widespread adoption, but developing a comprehensive security plan is challenging without a thorough analysis of potential vulnerabilities.To address this, a novel security-by-design framework for Big Data deployment on cloud computing, termed Big Cloud, is proposed in this article. The framework leverages systematic security analysis methodologies and automated assessment tools to map domain-specific security knowledge to design best practices. Validation of the framework was conducted through an Apache Hadoop stack use case, demonstrating its effectiveness in enhancing security awareness and reducing design time.The study also evaluates the framework's strengths and limitations, identifying key challenges in the Big Cloud domain.