A Bloom Filter-Based Cryptographic Approach for Secure Cloud Data Management

Abstract

This paper examines cryptographic algorithms that are based on Bloom Filters. These algorithms are capable of regulating access and safeguarding cloud data. These algorithms provide solutions that are efficient, scalable, and privacy-preserving. The proposed approach minimizes the amount of data storage and computer work. This is accomplished through the use of advanced cryptography and Bloom filters. This expedites the detection of unauthorized data access, user authentication, and membership verification. Bloom filters are an excellent choice for cloud big data management due to their ability to rapidly process queries and provide detailed descriptions of large datasets. This makes them the optimal choice for managing big data transactions. Protecting data from data breaches, hostile intrusions, and unauthorized changes, the framework implements encryption algorithms, hash functions, and secure key management. The results of the tests indicate that the proposed method enhances security performance, decreases false positives, and increases operational efficiency in comparison to conventional cloud security approaches. The research determined that the security of cloud storage and data in distributed computing environments can be enhanced by Bloom filter-based cryptographic models.