Study on Codon Modulo Cryptosystem for Privacy Preservation of Vertically Partitioned Outsourced Data

Abstract

Data passes crossways the cloud by the methods of assorted way. It is fundamental to safeguard the data from unapproved users to access the information in any structure. The information refuge is guaranteed by changing a plain text into an incomprehensible configuration by encoding text utilizing cryptographic calculations and these techniques are espoused for scrambling the text to made sure about their data from aggressors to guarantee data protection. Deoxyribo Nucleic Acid (DNA) is an encryption method utilized to provide security to the distributed computing data. In this paper Codon Modulo Cryptography-based Algorithm for vertically partitioned cloud data is applied to provide security for the transactional data. Affiliation Rule Mining and Frequent Itemset strategies are applied to aggregate the Association Rules among the Frequent Items in a scrambled exchange of vertically partitioned information base. The exhibition of Rule Mining calculations such as Apriori, FP-Growth and Eclat with the proposed Codon Modulo algorithm is contrasted with the conventional Homomorphic Encryption. The result exhibits that the proposed algorithm outperforms the Homomorphic Encryption in performance with a high-security level.