The Bloom filter remains one of the most influential constructs in probabilistic computation, a structure that achieves a mathematically elegant balance between accuracy, space efficiency, and computational speed. Since the original formulation of Dr. Burton H. Bloom in 1970, its design principles have expanded into a family of approximate membership query (AMQ) structures that now underpin a wide spectrum of modern computational systems. This review synthesizes the theoretical, algorithmic, and applied dimensions of Bloom filters, tracing their evolution from classical bit-vector models to contemporary learned and cryptographically reinforced variants. It further underscores their relevance in artificial intelligence and blockchain environments, where they act as relevance filters. Core developments, which include counting, scalable, stable, and spectral filters, are outlined alongside information-theoretic bounds that formalize their optimality. The analysis extends to adversarial environments, where cryptographic hashing and privacy-oriented adaptations enhance resilience under active attack, and to data-intensive domains such as network systems, databases, cybersecurity, and bioinformatics. Through the integration of historical insight and contemporary advances in learning, security, and system design, the Bloom filter emerges not merely as a data structure but as a unified paradigm for computation under uncertainty. The results presented in this review support practical advances in network traffic control, cybersecurity analysis, distributed storage systems, and large-scale data platforms that depend on compact and fast probabilistic structures.
Gagniuc et al. (Thu,) studied this question.