Probabilistic data structures are so popular in membership queries, network applications, and so on. Bloom Filter and Cuckoo Filter are two popular space efficient models that incorporate in set membership checking part of many important protocols. They are compact representation of data that use hash functions to randomize a set of items. Being able to store more elements while keeping a reaso...

This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter, a compact data structure supporting set insertion and membership queries, has found wide application in databases, storage systems, and networks. Because the Bloom filter performs frequent random reads and writes, it is used almost exclusively in RAM, limiting the size of the sets it can repres...

A Bloom filter is a compact data structure that supports membership queries on a set, allowing false positives. The simplicity and the excellent performance of a Bloom filter make it a standard data structure of great use in many network applications. In reducing the false positive rate of a Bloom filter, it is well known that the size of a Bloom filter and accordingly the number of hash indice...

Bloom filters are a well known data-structure that supports approximate set membership queries that report no false negatives. Each element in the universe represented by the bloom filter is associated with k random bits in the structure. Traditional bloom filters, therefore, require k non-local memory operations to insert an element or perform a lookups. For very large bloom filters, these k l...

A Bloom filter is a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, therefore, many applications use a counting Bloom filter allowing the deletion. This paper proposes a generalization of the counting Bloom filters approach, called “autoscaling Bloom filters”, which allows elastic adjustment of its capacity with prob...

Last decade is characterised by the convergence of pervasive technologies including wireless communication, smart devices and the Internet. The resulting Internet of the things enables not-only users but also things to access resource anywhere, anytime. In practice, data is ubiquitously flowed relying on global wireless connectivity (e.g., 3G) or on ad hoc-based local area networking, as enable...

A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information, and has broad applications. In this paper, we generalize the traditional Bloom filter to Adaptive Bloom Filter, which incorporates the information on the query frequencies and the...

This paper presents payload inspection for identification of spam files using bloom filter in dual core processor. Spam files flood the Internet in an attempt to dump the messages on recipients who do not intend to receive it. Spam costs the sender very little to send and most of the costs are levied to the recipients or the carriers. The proposed system identifies and filters the incoming spam...

Understanding and Improving Bloom Filter Configuration for Lazy Address-Set Disambiguation Mark C. Jeffrey Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2011 Many parallelization systems detect memory access conflicts across concurrent threads by disambiguating address-sets using bit-vector-based Bloom filters, which are efficient, bu...