BLOOM FILTER-BASED CRYPTOGRAPHIC TECHNIQUES FOR CLOUD DATA SECURITY

Abstract

This paper discusses Bloom Filter-based cryptographic algorithms that can be employed to securely store data and regulate access in cloud computing environments. These algorithms provide solutions that are effective, scalable, and privacy-preserving. The proposed approach minimizes the volume of data that must be stored and the work that must be performed on a computer by incorporating Bloom Filters and sophisticated cryptographic approaches. It also expedites the process of identifying unlawful data access, authenticating users, and verifying membership. In the cloud, bloom filters are an excellent tool for managing big data transactions, as they can rapidly process queries and provide a detailed description of large datasets. In order to safeguard data from cyber threats such as data breaches, hostile intrusions, and unauthorized modifications, the framework includes encryption algorithms, hash functions, and secure key management methods. The proposed method enhances security performance, reduces the number of false positives, and enhances operational efficiency in comparison to conventional cloud security practices, as demonstrated by tests. The paper demonstrated that Bloom Filter-based cryptographic models can enhance the dependability of data security in contemporary distributed computing settings and enhance the security of cloud storage systems.