Data Security in Distributed Big Data Systems: Protecting PII

Abstract

This review explores recent advancements in data security methodologies for distributed systems used in processing big data. With the proliferation of cloud, fog, and edge computing, protecting personally identifiable information (PII) has become a key priority. The paper categorizes and evaluates modern solutions, including cryptographic schemes (e.g., homomorphic encryption, differential privacy), access control mechanisms (ABAC, IAM), secure multi-party computation (SMPC), AI-based analytics for threat detection and privacy-preserving model training, and blockchain applications for decentralized access control and data integrity. A comparative framework illustrates the strengths and limitations of these methods across different distributed environments. The review concludes with a call for multi-layered, convergent security strategies to meet the growing demands of data protection in distributed big data ecosystems.