Zero-Trust Cryptographic Protocols and Differential Privacy Techniques for Scalable Secure Multi-Party Computation in Big Data Analytics

Abstract

This research explores the integration of zero-trust cryptographic protocols and differential privacy techniques to establish scalable secure multi-party computation in the context of big data analytics. The study delves into the challenges of collaborative data processing and presents a comprehensive framework that addresses the intricate balance between security, scalability, and privacy. The framework focuses on zero-trust cryptographic protocols, advocating for a fundamental shift in trust assumptions within distributed systems. Differential privacy techniques are then seamlessly integrated to preserve individual privacy during collaborative data analytics. This model employs a layered approach and distributed architecture and leverages serverless and edge computing fusion to enhance scalability and responsiveness in dynamic big data environments. This also explores the optimization of computational resources and real-time processing capabilities through serverless and edge computing fusion. A distributed architecture facilitates efficient collaboration across multiple parties, allowing for seamless data integration, preprocessing, analytics, and visualization. Privacy preservation takes centre stage in the big data privacy component of the framework. Context-aware attribute analysis, distributed federated learning nodes, and Attribute-Based Access Control (ABAC) with cryptographic enforcement are introduced to ensure fine-grained access control, contextual understanding of attributes, and collaborative model training without compromising sensitive information. Smart Multi-Party Computation Protocols (SMPCP) further enhance security, enabling joint computation of functions over private inputs while ensuring the integrity and immutability of data transactions. In essence, the achieved results manifest a paradigm shift where the layered approach, distributed architecture, and advanced privacy techniques converge to heighten data security, drive efficient computation, and robustly preserve privacy in the expansive landscape of big data analytics. Fault tolerance and resource utilization exhibit significant advancements, with fault tolerance experiencing a 10% boost and resource utilization optimizing by 12%. These enhancements underscore the robustness and efficiency of the system's design, ensuring resilience and optimized resource allocation.