Evaluating the effectiveness of homomorphic encryption in big data: a descriptive and diagnostic analysis

Abstract

The ever-increasing volume of data generated from various sources, including the Internet of Things (IoT) and digital channels, presents a significant challenge for organizations. This rapid growth often necessitates offloading data analysis to the cloud due to limitations on local server capacity. However, security concerns arise when analyzing sensitive data in the cloud environment. Traditional encryption methods, while effective in protecting data at rest, require decryption prior to analysis, potentially exposing sensitive information. On the other hand, Homomorphic Encryption (HE) is gaining popularity as it – offers a solution by enabling computations to be performed directly on encrypted data. This paper investigates the effectiveness of homomorphic encryption on big data through descriptive and diagnostic analyses. Result suggest that this approach is better in terms of execution time and is particularly well-suited for big data analytics due to its inherent scalability.