Examining the function of Merkle trees in enhancing security within big data technologies

Abstract

The main purpose of this article is to study the structural characteristics of the Big Data paradigm, which presents serious data confidentiality, integrity, and security challenges. Big data technology and its associated services have become the central topic of numerous scientific studies and popular applications due to the rapid progress in this field and the development of data analysis solutions. Comprehensive solutions and innovative technologies are required to protect confidential information. However, current evaluations of Merkle tree methods primarily focus on the metrics of traditional cryptosystems. This article describes the Big Data Security ecosystem and emphasizes the importance of maintaining data integrity and authenticity during exchanges in various fields. The authors propose that Merkle tree technology can effectively solve these issues using a quantum cryptosystem algorithm, providing secure and efficient data exchange. In this work, we conducted experimental research using Python and a quantum-computing library to obtain accurate test data. The scientific novelty of the work is the development of a new method and technology for a modified Merkle signature scheme to select the optimal computational efficiency and security. Finally, experimental studies have shown that researchers can use trusted technologies to perform a complete probabilistic analysis of Merkle tree data, which allows for safe end-to-end Big Data exchanges.