Enhancing Privacy in Big Data Analytics Through Encrypted Computational Techniques and Secure Multi-Party Computation Strategies

Abstract

The increasing volume and sensitivity of data used in big data analytics necessitate advanced privacy-preserving techniques to protect against unauthorized access and data breaches. This paper presents a comprehensive exploration of encrypted computational techniques and secure multi-party computation (MPC) strategies as pivotal solutions for enhancing privacy in big data analytics. Encrypted computational techniques, including Homomorphic Encryption, Secure Enclaves, and Zero-Knowledge Proofs, enable secure data processing by allowing computations on encrypted data, thus ensuring the confidentiality and integrity of the underlying data. On the other hand, Secure Multi-Party Computation (MPC) facilitates collaborative data analysis among multiple entities without revealing their individual datasets, leveraging methods such as Secret Sharing, Garbled Circuits, and Federated Learning. While these approaches offer robust privacy protections, they also introduce challenges related to performance, complexity, and scalability. The paper discusses these challenges and highlights ongoing research and development efforts aimed at optimizing and simplifying these technologies for broader adoption. Through a detailed examination of these privacy-enhancing technologies, this paper underscores their critical role in securing big data analytics and outlines future directions for making these solutions more efficient and accessible.