HexaMorphHash HMH- Homomorphic Hashing for Secure and Efficient Cryptographic Operations in Data Integrity Verification

TL;DR

This paper proposes HexaMorphHash, a homomorphic hashing scheme that enables efficient, incremental data integrity verification in distributed systems, offering constant-time updates, strong security, and scalability against quantum threats.

Contribution

Introduction of HexaMorphHash, a lattice-based homomorphic hash function that allows constant-time incremental updates with a fixed digest size, enhancing efficiency and security in distributed data integrity verification.

Findings

Achieves constant-time, incremental updates with fixed digest size.

Provides strong security based on the SIS problem, resistant to quantum attacks.

Outperforms existing methods in computational efficiency and scalability.

Abstract

In the realm of big data and cloud computing, distributed systems are tasked with proficiently managing, storing, and validating extensive datasets across numerous nodes, all while maintaining robust data integrity. Conventional hashing methods, though straightforward, encounter substan tial difficulties in dynamic settings due to the necessity for thorough rehashing when nodes are altered. Consistent hashing mitigates some of these challenges by reducing data redistribution; however, it still contends with limitations in load balancing and scalability under intensive update conditions. This paper introduces an innovative approach using a lattice based homomorphic hash function HexaMorphHash that facilitates constant time, incremental updates while preserving a constant digest size. By utilizing the complexity of the Short Integer Solutions SIS problem, our method secures strong protective measures, even against quantum threats. We further com pare our method with existing ones such as direct signatures for each update, comprehensive database signing, Merkle tree based techniques, AdHash, MuHash, ECMH, and homomorphic sig nature schemes highlighting notable advancements in computational efficiency, memory usage, and scalability. Our contributions present a viable solution for frequent update dissemination in expansive distributed systems, safeguarding both data integrity and system performance.