1 University of California Irvine, CA 92697, United States.
2 Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India.
3 KL University, Vijayawada, Andhra Pradesh, India.
World Journal of Advanced Engineering Technology and Sciences, 2025, 14(03), 105-119
Article DOI: 10.30574/wjaets.2025.14.3.0068
Received on 01 January 2025; revised on 07 February 2025; accepted on 10 February 2025
The advent of distributed cloud systems has revolutionized data storage and access, providing flexibility and scalability across various industries. However, these benefits come with significant challenges, particularly concerning privacy and adherence to net neutrality principles. Traditional encryption methods often impose a trade-off between security and performance, leading to compromises that may affect privacy or violate net neutrality. This paper introduces a Segmented Encryption Algorithm (SEA) designed to enhance privacy without undermining the performance and neutrality of cloud services.
Our proposed SEA operates by segmenting data into smaller, manageable blocks, each encrypted under a unique key while maintaining a uniform service rate across all data packets. This segmentation allows for more granular control over encryption, reducing the vulnerability associated with single-point encryption failures and enabling efficient key management. Moreover, by encrypting segments independently, SEA minimizes the computational overhead typically associated with encryption processes, thus maintaining high throughput and low latency in cloud operations.
The architecture of SEA is built upon a hybrid cryptographic framework that combines symmetric and asymmetric encryption techniques. Symmetric encryption is used for data segments due to its lower computational requirements, whereas asymmetric encryption secures the keys, enhancing the overall security of the system. This hybrid approach not only strengthens data protection but also streamlines the encryption process, allowing for real-time data access and processing without significant delays.
To evaluate the effectiveness of SEA, we conducted a series of experiments in a simulated cloud environment. These experiments measured the algorithm’s impact on latency, throughput, and CPU utilization compared to conventional encryption methods. The results demonstrated that SEA maintains net neutrality by treating all data packets equally, without prioritizing or discriminating based on content, source, or destination. Privacy tests confirmed that SEA provides robust protection against various security threats, including brute force attacks and data breaches.
Further, the implementation of SEA in a distributed cloud environment showcased its adaptability and efficiency. By leveraging decentralized encryption management, the algorithm enhances the fault tolerance of cloud systems, reducing the risks associated with centralized key storage and management. This decentralized approach also supports compliance with global data protection regulations, such as GDPR and CCPA, by allowing data to be encrypted and managed locally, according to regional legal requirements.
In conclusion, the Segmented Encryption Algorithm represents a significant advancement in the field of cloud security. By addressing the core issues of privacy and net neutrality in distributed cloud systems, SEA sets a new standard for secure, equitable, and efficient cloud services. Its scalable and flexible design makes it suitable for a wide range of applications, from enterprise cloud solutions to public cloud services, promising a safer and more compliant digital environment for all users
Cloud Computing; Data Privacy; Net Neutrality; Distributed Systems; Symmetric Encryption; Asymmetric Encryption; Fault Tolerance; GDPR Compliance
Preview Article PDF
Soham Sunil Kulkarni, Anant Kumar and Arpit Jain. Segmented encryption algorithm for privacy and net neutrality in distributed cloud systems. World Journal of Advanced Engineering Technology and Sciences, 2025, 14(03), 105-119. Article DOI: https://doi.org/10.30574/wjaets.2025.14.3.0068.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0