Enhancing cloud security using crypto-deep neural network for privacy preservation in trusted environment

The users in communication interact over cloud for data exchange. The participants are in various levels, and their expectation varies on the nature of interactions. Though building a common platform for interactions over cloud environment is difficult, there is scope for developing security solutions that can ensure confidentiality of data exchange. In the proposed model distributed secure outsourcing scheme is enhanced using crypto-deep neural network. The proposed model has cloud server, web server, data center and cloud agent. The model mainly targets in handling impersonation attack using crypto-deep neural network cloud security (CDNNCS). The proposed framework is suitable for enhancing the level of trust among cloud users in comparison to secure linear algebraic equation scheme. The performance has been presented in terms of parameters namely Delay, Jitter, Throughput and Goodput. From the results it can be observed that with CDNNCS packet loss has been reduced by 10% and the response time has been increased by 5% in comparison to existing approach.