Enhanced Privacy and Security Maintenance to Preserve BigData using Cipher Policy

As the use of big data in cloud computing continues to increase at an exponential rate, privacy concerns have emerged as a major concern. Every person and business should make data protection a top priority. Security risks may arise during transmission from client to cloud server. Cloud computing technology makes it possible for mobile clients to access the large data stores. With the use of a unified parallel and distributed encryption and decryption system, mobile clients and cloud computing may be made more secure. The security of older systems, such as those that rely on plaintext on the cloud, is questionable at best. Dynamic Data Encryption Strategy (DDES) is the suggested approach that aims to improve the effectiveness of privacy safeguards. The Dynamic Encryption Determination (DED) method was designed to dynamically alternate data packages for encryptions under varying time restrictions, and it was the primary algorithm underpinning the DDES paradigm. Our suggested solution utilizes privacy categorization techniques to selectively encrypt data while working within time limitations. Utilizing a selective encryption strategy while still meeting the necessary execution time constraints, this method aims to increase the privacy protection scope. Our experimental results assess DDES's performance and demonstrate the privacy boost.