Enhanced Computation Architecture for Big Data Analytics Using Encryption Algorithm

Abstract

Big data infrastructure needs to be structured in the most critical aspects and wisely calculate how large data applications are managed in order to achieve the most important security issues required. One of them is privacy is a related feature as users can share more and more personal data and content and public clouds on social networks through their devices and computers. The previous system have some drawbacks, it is not secure the sensitive data storage security of privacy issues in the big data. However, the existing encryption system for data cannot protect the access mode, and it can also leak sensitive information. To overcome the issues in this work proposed the method Transparent Secure Hashing Data Optimized Privacy Protection Encryption (TSHDOPPE) Algorithm for Encryption of data can prevent permission to use the unwanted users' associated data storage system of data. Less storage leakage overhead and efficiency are proposed. It includes stored in a transparent data protected for data at rest and could not leak the Data for data in Transit. Non-relational data storage and protection data storage has been a TSHDOPPE algorithm used to ensure the transaction log. In the data used for stored in cloud computing, managing user data optimizing the Improved Deep Neural Network (IDNN) is reduces difficult and reduces the cost of maintaining data. Unprotected data in Transit or at rest, or vulnerable to attacks, companies are also effective security measures that provide protected data with strong data protection between the device and the network in these conditions.