A Bloom Filter (BF) is a data structure suitable for performing set membership queries very efficiently. A Standard Bloom Filter representing a set of n elements is generated by an array of m bits and uses k independent hash functions. Bloom Filters have some attractive properties including low storage requirement, fast membership checking and no false negatives. False positives are possible bu...

Introduction: Bloom filters [1] are a space-efficient, probabilistic data structure for representing a list of elements (for example, a list of strings). A Bloom filter is an array of m bits. A string is mapped into a Bloom filter by inputting it to a group of k hash functions resulting in k array positions. Each indexed array position is set to 1. A string is tested for membership by inputting...

In this paper, we propose a privacy-preserving variant of Bloom-filters. The Bloom-filter has many applications such as hash-based IP-traceback systems and Web cache sharing. In some of those applications, equipping the Bloom-filter with the privacy-preserving mechanism is crucial for the deployment. In this paper, we propose a cryptographically secure privacy-preserving Bloom-filter protocol. ...

Counting Bloom Filters (CBFs) are widely employed in many applications for fast membership queries. CBF works on dynamic sets rather than a static set via item insertions and deletions. CBF allows false positive, but not false negative. The Bh-Counting Bloom Filter (Bh-CBF) and Variable Increment Counting Bloom Filter (VI-CBF) are introduced to reduce the false positive probability, but they su...

In distributed hash tables, much communication traffic comes from multi-word searches. The aim of this work is to reduce the amount of traffic by using a bloom filter, which is a space-efficient probabilistic data structure used to test whether or not an element is a member of a set. However, bloom filters have a limited role if several sets have different numbers of elements. In the proposed m...

This paper presents an algorithm for exact pattern matching based on a new type of Bloom filter that we call a feed-forward Bloom filter. Besides filtering the input corpus, a feed-forward Bloom filter is also able to reduce the set of patterns needed for the exact matching phase. We show that this technique, along with a CPU architecture aware design of the Bloom filter, can provide speedups b...

Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to model the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, clarifying what guarantees can and cannot be associated with such a structure.

Bloom filters provide a spaceand time-efficient mean to check the inclusion of an element in a set. In some applications it is beneficial, if the set represented by the Bloom filter is only revealed to authorized parties. Particularly, operations data in supply chain management can be very sensitive and Bloom filters can be applied to supply chain integrity validation. Despite the protection of...

The Bloom filter is a simple random binary data structure which can be efficiently used for approximate set membership testing. When testing for membership of an object, the Bloom filter may give a false positive, whose probability is the main performance figure of the structure. We complete and extend the analysis of the Bloom filter available in the literature by means of the γ-transform appr...

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes–no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to...