An Efficient Integrity Controlled CP-ABE(IC-CPABE) for Audio Cloud Block Chain Transactions

Abstract

For increasing size of applicants data, in audio-based application, That motivate poor security level of audio data and some time results in invasion our personal data by third party. To fill this loop hole there has no any work on data integrity improvement, Their planning to do a novel framework. In this framework, an optimal data integrity algorithm, Group-wise Attribute Initialization and Policy construction. It would be implicit in each group, in data encoding and decoding by the activation of attribute-based encryption policies. To provide the integrity for audio data, there has a novel hybrid non-linear integrity-based dynamic hashing methodology.Further more, there has a hybrid Ciphertext-Policy Attribute-Based Encryption for data encrypting and decrypting. Next, under ensemble and parallel architecture, the IABE model verification against KABE and TRAK-CPABE. It does a comparative study on encryption and decryption runtimes, on different groups of size, in audio data.The group created from combination of some chosen attribute in IABE scheme, and it's consists of heterogeneous groups and the groups are connected by sub groups of different size. The runtime compare between IABE scheme, classical KABE (Cloudfile_KABE) and TRAK-CPABE (TRAK-CPABE).In encrypted audio data, the hash bit variations is the key parameter, which check as a different identifier and it helps to communicate audio data encoded or it might utilize as security process. The same parameter computed in IABE model, with the existing known model: Cloudfile_KABE_MD5 and TRAK-CPABE_SHA-512 for different group size.The work also works on Hashing technique of transcription audio file, to evaluate memory usage and execution times. It used the real samples of audio bunch like, 'KITCHEN', 'microwave','scream2' and other.The analysis is based on execution time in select different file size, by using directly hash value, It returns different hash value compared with the previously value used. If system again replay any data within the largest hash size.