A Fractal Coding and TDES-DIMA Based Framework for Privacy-Preserving Big Data Storage in Cloud Environments

Abstract

The rapid expansion of the big data in cloud facilities has posed immense challenges in terms of storage efficiency, privacy as well as security. To address these issues, this study proposes a Fractal Coding-based Privacy-Preserving Framework with a dynamic trusted authentication architecture to handle big data in cloud systems safely. First the framework divides raw information into blocks and encodes them to small fractal codes minimizing overhead of storage and transmission yet preserving the structure of the information. Then the privacy levels of each blocks are determined by the sensitivity allocation mechanism and much protection is guaranteed on highly sensitive data through assigning the privacy level to the respective divided block. Finally, to strengthen confidentiality, Triple Data Encryption Standard (TDES)-Dynamic Intelligent Multiple Access (DIMA) module added which engages extended TDES encryption together with dynamic authentication to withhold brute force attack and unauthenticated access. The combination of these design balances compression efficiency, the preservation of privacy, and cloud security. Experimental analysis shows that the proposed framework provides a faster decryption, a high transmission efficiency, high data confidentiality of 95% and minimum CPU consumption of 24%.