Scalable Approaches for Enhancing Privacy in Blockchain Networks: A Comprehensive Review of Differential Privacy Techniques

Abstract

The rapid adoption of blockchain technology in a number of industries, such as supply chain management, healthcare, and finance, has intensified concerns surrounding data privacy. As sensitive information is stored and shared on decentralized networks, the inherent cryptographic mechanisms of blockchain provide robust security. However, the transparency of public ledgers can unintentionally expose sensitive data, resulting in potential privacy risks and regulatory challenges. Differential privacy has emerged as a promising approach to protect individual data while preserving the usability of shared datasets. By enabling data analysis without revealing individual data points, differential privacy is well-suited for anonymizing transactions, smart contract interactions, and other blockchain activities. However, integrating differential privacy into blockchain systems presents several challenges, including ensuring scalability, balancing privacy with data utility, and managing computational overhead. This review, "Scalable Approaches for Enhancing Privacy in Blockchain Networks: A Comprehensive Review of Differential Privacy Techniques," examines 50 recent studies published between 2023 and 2024 that investigate differential privacy techniques in blockchain networks. It highlights various scalable approaches and their effectiveness in enhancing privacy. The findings indicate that these methods can significantly improve privacy protection, provide flexibility for both public and private blockchains, and assist in complying with regulatory requirements. This establishes differential privacy as a vital tool for secure blockchain implementation.