Scalable Data Storage in the Cloud: Securing Data with Optimized S-Boxes via Genetic Algorithms
Rahma Berchi, Sarra Cherbal, Lemia Louail, Assala Nacef, Lina Benchikh · 2024
Secure and scalable data storage poses significant challenges in decentralized applications. Traditional centralized systems are vulnerable to breaches, while blockchain technology, despite its advantages, faces high costs, limited capacity, and privacy concerns. This paper introduces a hybrid model that integrates the InterPlanetary File System (IPFS) for scalable storage with blockchain technology for secure access control, effectively addressing these issues. A key challenge in enhancing cryptographic security is achieving high non-linearity in Substitution Boxes (S-boxes) to resist cryptanalytic attacks. Many existing S-boxes fall short, compromising the security of sensitive data, such as healthcare records. We use genetic algorithms (GAs) to optimize S-boxes in order to mitigate this weakness. This results in a score of 106, a significant improvement in non-linearity, and increased resilience to various assaults, such as differential and linear cryptanalysis. In addition to optimizing the non-linearity of the S-boxes, our GA-based method improves data encryption prior to storing it on IPFS, hence increasing scalability and cost-effectiveness while significantly bolstering data security and integrity in decentralized systems. This innovation is particularly beneficial for high-security sectors like healthcare, where robust data protection is essential.