An Enhanced Big Data Handling Architecture for Privacy Preservation of Cloud Data

Abstract

The cloud is the leading technology in the IT world for storing and managing massive quantities of data. Protection and data privacy preservation are two of the most common concerns in big data. Confidential information must be secured from multiple unauthorized accesses in attempt to optimize its security. Different traditional cryptography algorithms have been used in the security of big data in the cloud to enhance privacy. Still, because of its reduced security, there are some privacy protection concerns. With the emergence of IoT-cloud-based devices, IoT has advanced significantly in the field of big data processing. The health-care system is one of the recent IoT-based Big data applications. To preserve the privacy of patient data, several studies are required. Data security and computing overheads are still major challenges in the IoT-cloud-based health system. To ensure the privacy of huge data, the ElGamal Elliptic Curve (EGEC) encryption technique is proposed. The results are analyzed and the comparison depicts the outperformance of the proposed system.