Enhanced Security Model in Cloud Using Neural Networks

Abstract

In order to improve redundancy elimination security is an important factor to be considered. To increase the security of cloud storages a new method of neural network based security enhancement has to be provided. Data confidentiality with sensitive data sets and provides data isolation. The dynamic fragmented component automatically extends and shrinks during insertion and deletion, respectively, and also provides explicit dynamic data support, including block update, delete, and append. The Neural Data Security model is used to encrypt and decrypt the sensitive data by using cryptography. It attains data security for public and private keys using cryptography using Neural Networks. The Data Security Model is more efficient and effective for all kinds of queries, and performance is high at the data confidentiality level. This model provides less expensive, higher performance and an expandable storage system to enhance the security.