Author Information

Vishnu Valleru – Independent Researcher, Austin, Texas | vishnuvalleru@gmail.com

Venkata Sai Mahesh Vuppalapati – Independent ML Researcher, San Francisco, USA | sai.vuppalapati@ieee.org

Abstract

With big data and widely distributed systems, confidentiality and integrity are major concerns across domains. This paper introduces **Holographic Data Partitioning (HDP)** to enhance security and reliability within mega-scale distributed environments. Inspired by holography, HDP distributes fragments of information across nodes, ensuring protection against unauthorized access and data tampering.

Using real-world datasets like **AWS public datasets** and **GCP traffic logs**, HDP demonstrates improved security metrics while maintaining system performance. The framework facilitates cross-domain data integration while preserving integrity across diverse organizational boundaries. The study confirms HDP’s effectiveness in minimizing security threats while ensuring scalability and efficiency.

Index Terms

Holographic Data Partitioning, Data Confidentiality, Data Integrity, Distributed Systems, Cross-Domain Security

Conclusion

HDP is a novel framework designed to embed data confidentiality and integrity into ultra-large-scale distributed systems. By leveraging **error-correcting codes and holography-inspired data distribution**, HDP addresses conventional partitioning limitations that often overlook security.

Experimental validation using **GCP Traffic Logs** demonstrates HDP’s ability to maintain data accuracy and throughput while withstanding **up to 30% node failures**. The framework efficiently scales while ensuring robust security without compromising performance. These findings suggest that **HDP is a promising solution for next-generation cloud and big data platforms** that demand high-security standards.