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Bloom Filter is extensively deployed data structure in various applicationsand research domain since its inception. Bloom Filter is able to reduce thespace consumption in an order of magnitude. Thus, Bloom Filter is used to keepinformation of a very large scale data. There are numerous variants of BloomFilters available, however, scalability is a serious dilemma of Bloom Filterfor years. To solve this dilemma, there are also diverse variants of BloomFilter. However, the time complexity and space complexity become the key issueagain. In this paper, we present a novel Bloom Filter to address thescalability issue without compromising the performance, called scaleBF. scaleBFdeploys many 3D Bloom Filter to filter the set of items. In this paper, wetheoretically compare the contemporary Bloom Filter for scalability and scaleBFoutperforms in terms of time complexity.

scaleBF: A High Scalable Membership Filter using 3D Bloom Filter