Traditional multicasting techniques give senders and receivers little control for who can receive or send to the group and enable end hosts to attack the multicast infrastructure by creating large amounts of group specific state. Bloom filter based multicast has been proposed as a solution to scaling multicast to large number of groups. In this paper, we study the security of multicast built on...

Bloom filters and Counting Bloom Filters (CBFs) are widely used in networking device algorithms. They implement fast set representations to support membership queries with limited error. Unlike Bloom filters, CBFs also support element deletions. In the first part of the talk, I will introduce a new general method based on variable increments to improve the efficiency of CBFs and their variants....

Bloom filters are a randomized data structure for membership queries dating back to 1970. Bloom filters sometimes give erroneous answers to queries, called false positives. Bloom analyzed the probability of such erroneous answers, called the false-positive rate, and Bloom’s analysis has appeared in many publications throughout the years. We show that Bloom’s analysis is incorrect and give a cor...

Where distributed agents must share voluminous set membership information, Bloom filters provide a compact, though lossy, way for them to do so. Numerous recent networking papers have examined the trade-offs between the bandwidth consumed by the transmission of Bloom filters, and the error rate, which takes the form of false positives. This paper is about the retouched Bloom filter (RBF). An RB...

A quantum Bloom filter is a spatially more efficient data structure which used to represent set of $n$ elements by using notation="LaTeX">$O({{\rm{log}}nk})$ qubits. In this article, we define and design its ...

Bloom Filters (BF) [1] are space-e cient datastructures that allow membership queries from a set. In most recent years they have gained great momentum and various tweaks to them have been proposed to achieve speci c goals. Dynamic Bloom Filters (DBF) have been proposed [2] as a method to implement Bloom Filters in a scalable environment, i.e. where the nal size of a dataset is not known in adva...

Ready simulation has proven to be one of the most significant semantics in process theory. It is at the heart of a number of general results that pave the way to a comprehensive understanding of the spectrum of process semantics. Since its original definition by Bloom, Istrail and Meyer in 1995, several authors have proposed generalizations of ready simulation to deal with internal actions. How...

A technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false ...

Shifting Bloom filters use location offset to encode state values for a set of elements. In spite its novelty, classification error rate inherent shifting needs be improved. this paper, we design hierarchical bloom filter address issue. Firstly, elements in are partitioned into disjoint groups. Each group is assigned unique number. Then these numbers implicitly encoded by major filter(MShi ftBF...

Where distributed agents must share voluminous set membership information, Bloom filters provide a compact, though lossy, way for them to do so. Numerous recent networking papers have examined the trade-offs between the bandwidth consumed by the transmission of Bloom filters, and the error rate, which takes the form of false positives, and which rises the more the filters are compressed. In thi...