Enhancing the Robustness of Bloom Filters by Introducing Dynamicity

A Bloom Filter (BF) is a compact and space-efficient data structure that efficiently handles membership queries on infinite streams with numerous unique items. They are probabilistic data structures and allow false positives to avail the compactness. While querying for an item’s membership in the structure, if it returns true, the item might or might not be present in the stream, but a false response guarantees the item's absence. Bloom filters are widely used in real-world applications such as networking, databases, web applications, email spam filtering, biometric systems, security, cloud computing, and distributed systems due to their space-efficient and time-efficient properties. Bloom filters offer several advantages, particularly in storage compression and time-efficient data lookup. Additionally, the use of hashing ensures data security, i.e., if the BF is accessed by an unauthorized entity, no enrolled data can be reversed or traced back to the original content. In summary, BFs are powerful structures for storing data in a storage-efficient approach with low time complexity and high security. However, a disadvantage of the traditional Bloom filters is, they do not support dynamic operations, such as adding or deleting elements. Therefore, in this project, the idea of a Dynamic Bloom Filter has been demonstrated that offers the dynamicity feature that allows the addition or deletion of items. By integrating dynamic capabilities into Standard Bloom filters, their functionality, and robustness are enhanced, making them more suitable for several applications. For example, in a perpetual inventory system, inventory records are constantly updated after every inventory-related transaction, such as sales, purchases, or returns. In banking, dynamic data changes throughout the course of transactions. In the healthcare domain, hospitals can dynamically update and delete patients' medical histories.