Preserving Big Data Privacy in Cloud Environments Based on Homomorphic Encryption and Distributed Clustering

Abstract

Cloud computing has grown in popularity in recent years because to its efficiency, flexibility, scalability, and the services it provides for data storage and processing. Still, big businesses and organizations have severe concerns about protecting privacy and data security while processing these massive volumes of data.

This paper proposes approach that intends to enhance efficiency in delivering advanced data protection, hence filling security holes, by enhancing data protection from various big data sources. A partial homomorphic encryption system is used to encrypt data created by many sources or users and processed in the cloud without decrypting it, hence protecting data from attackers. Extremely Distributed Clustering (EDC) has also been applied to partition large datasets into many cloud computing node subsets. This method can ensure privacy and protect data while also enhancing the effectiveness and performance of big data analytics. According to the results, the proposed technique was faster and gave improved encryption performance by around 23-28%.