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This work presents recent developments in hashing algorithmdesign. Hashing is the process of creating a short digest(i.e., 64 bits) for a message ofarbitrary length, for exam-ple 20 Mbytes. Hashing algorithms were firstused for sear-ching records in databases; they are central for digital si-gnature applications and are used for authenticationwithoutsecrecy. Covering all practical and theoretical issuesrelatedto the design of secure hashing algorithms the bookis self contained; it includes an extensive bibliography onthe topic. ... Read more

Customer Reviews (1)

explains many hash algorithms
Hashing is vital in many computational applications. It forms the backbone of searching, for example. So this book is very useful in giving a detailed explanation of the theory behind various hashing methods.

It is slightly dated. It does not describe the Simple Hashing Algorithmn 2, which is currently considered the most secure method. But the ideas in the book are still applicable. Useful if you want a rigorous understanding of the theoretical underpinnings of the field. Or if you want to start designing your own hash algorithm. ... Read more

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Hashing, a commonly used technique for arranging data to facilitate rapid searches, is discussed from several different perspectives as an efficient solution to the classical problem of information storage and retrieval.The underlying theme is close cooperation between the analysis of algorithms and the computer world.To increase the work's accessibility to computer scientists, algorithms are given both in English and in a variant of the well-known language Pascal. Designed to appeal to as wide an audience as possible, this book serves both as a graduate text in analysis of algorithms and as a professional reference for computer scientists and programmers. ... Read more

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Written by one of the developers of the technology, Hashing is both a historical document on the development of hashing and an analysis of the applications of hashing in a society increasingly concerned with security. The material in this book is based on courses taught by the author, and key points are reinforced in sample problems and an accompanying instructor s manual. Graduate students and researchers in mathematics, cryptography, and security will benefit from this overview of hashing and the complicated mathematics that it requires. ... Read more

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Chapters: Hash table, HMAC, Collision, Rabin-Karp string search algorithm, Bloom filter, Cryptographic hash function, Extendible hashing, Comparison of cryptographic hash functions, Cuckoo hashing, Universal hashing, Hash join, Open addressing, Hash tree, Coalesced hashing, Hopscotch hashing, Linear hashing, Consistent hashing, Perfect hash function, SUHA, Koorde, Quadratic probing, Hash list, Linear probing, Index mapping, Hash consing, Double hashing, Hash array mapped trie, Dynamic perfect hashing, 2-choice hashing, Dirhash, Stable hashing,. Source: Wikipedia. Pages: 153. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: In computer science, a hash table or hash map is a data structure that uses a hash function to map identifying values, known as keys, (e.g., a person's name) to their associated values (e.g., their telephone number). The hash function is used to transform the key into the index (the hash) of an array element (the slot or bucket) where the corresponding value is to be sought. Ideally, the hash function should map each possible key to a unique slot index, but this ideal is rarely achievable in practice (unless the hash keys are fixed; i.e. new entries are never added to the table after creation). Most hash table designs assume that hash collisions-the situation where different keys happen to have the same hash value-are normal occurrences and must be accommodated in some way. In a well-dimensioned hash table, the average cost (number of instructions) for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key-value pairs, at constant average (indeed, amortized) cost per operation. In many situations, hash tables turn out to be more efficient than search trees or any other table lookup st...http://booksllc.net/?id=13833 ... Read more

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Chapters: Hash function, Pearson hashing, Rolling hash, Perfect hash function, Fowler-Noll-Vo hash function, Zobrist hashing, List of hash functions, MurmurHash, Jenkins hash function, Bitstate hashing, Locality preserving hashing, Hash filter, Morton number,. Source: Wikipedia. Pages: 54. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: A hash function is any well-defined procedure or mathematical function that converts a large, possibly variable-sized amount of data into a small datum, usually a single integer that may serve as an index to an array. The values returned by a hash function are called hash values, hash codes, hash sums, checksums or simply hashes. Hash functions are mostly used to speed up table lookup or data comparison tasks-such as finding items in a database, detecting duplicated or similar records in a large file, finding similar stretches in DNA sequences, and so on. A hash function may map two or more keys to the same hash value. In many applications, it is desirable to minimize the occurrence of such collisions, which means that the hash function must map the keys to the hash values as evenly as possible. Depending on the application, other properties may be required as well. Although the idea was conceived in the 1950s, the design of good hash functions is still a topic of active research. Hash functions are related to (and often confused with) checksums, check digits, fingerprints, randomization functions, error correcting codes, and cryptographic hash functions. Although these concepts overlap to some extent, each has its own uses and requirements and is designed and optimised differently. The HashKeeper database maintained by the American National Drug Intelligence Center, for instance, is more aptly described as a catalog of file fingerprints than of hash values. Hash ...http://booksllc.net/?id=13790 ... Read more

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The Information Age has enabled the search forinformation in ways never imagined before. The search criteria may be exact where the inputquery is expected to exactly match the search object,or fuzzy -- for instance image search, news search,and similar document search -- making the searchproblem much harder. Hashing is a simple andeffective method for exact search that uses a randomhash function to map items into buckets, often viewedas throwing balls into bins. This book studiesalgorithms for different kinds of search usinghashing and sketching, and some fundamental limits ofwhat can be realized using some of these approaches.For exact search, we will see how variants of balls- and-bins processes can be used to derive spaceefficient methods for maintaining hash tables.Forfuzzy search, we will see a variant of a special typeof hashing, called locality-sensitive hashing, thatuses linear space and how the underlying ideas can beused in the kd-tree data structure for improvedperformance. We will also probe the fundamentallimits of some of these approaches by showing lowerbounds on their performance. ... Read more

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Chapters: Splay Tree, Sparse Array, Extendible Hashing, Pairing Heap, Skew Heap, Search Data Structure, Finger Tree, Persistent Data Structure, Ternary Search Tree, Rope, Cover Tree, Hashed Array Tree, Double Hashing, Dsw Algorithm, B*-Tree, Difference List, Hash Map, Linear Probing, Judy Array, Hash Array Mapped Trie, Matrix Representation, Bk-Tree, Range Tree, Vp-Tree, Implicit Data Structure, R+ Tree, Exponential Tree, (A,b)-Tree, Htree, Scalar, Enterprise Data Modeling, Navigation Mesh, Link/cut Tree, X-Tree, Jump List, Self-Organizing List, Bang File, Fusion Tree, Goal Node, Adaptive Heap Sort, Metric Tree, 2-3 Heap, Linear Octrees, Trinomial Tree, B Sharp Tree, Bidirectional Map, Stable Hashing. Source: Wikipedia. Pages: 145. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: A splay tree is a self-balancing binary search tree with the additional property that recently accessed elements are quick to access again. It performs basic operations such as insertion, look-up and removal in O(log(n)) amortized time. For many non-uniform sequences of operations, splay trees perform better than other search trees, even when the specific pattern of the sequence is unknown. The splay tree was invented by Daniel Dominic Sleator and Robert Endre Tarjan in 1985. All normal operations on a binary search tree are combined with one basic operation, called splaying. Splaying the tree for a certain element rearranges the tree so that the element is placed at the root of the tree. One way to do this is to first perform a standard binary tree search for the element in question, and then use tree rotations in a specific fashion to bring the element to the top. Alternatively, a top-down algorithm can combine the search and the tree reorganization into a single phase. Good performance for a splay tree depends on...More: http://booksllc.net/?id=28382 ... Read more

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Australian ethnohistorian Greg Dening argues that there are two views that define the Pacific: a view from the sea (the view of those who arrived from elsewhere) and the view from the land (those who were already there). things of each possible relation hashing against one another is a series of poems that opens with the view from the sea and end with the view from the land and are about the ecological hashing that happens as these two views meet in Hawai'i. ... Read more

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This digital document is a journal article from Digital Investigation, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Hashing is a fundamental tool in digital forensic analysis used both to ensure data integrity and to efficiently identify known data objects. However, despite many years of practice, its basic use has advanced little. Our objective is to leverage advanced hashing techniques in order to improve the efficiency and scalability of digital forensic analysis. Specifically, we explore the use of Bloom filters as a means to efficiently aggregate and search hashing information. In this paper, we present md5bloom-an actual Bloom filter manipulation tool that can be incorporated into forensic practice, along with example uses and experimental results. We also provide a basic theoretical foundation, which quantifies the error rates associated with the various Bloom filter uses along with a simulation-based verification. We provide a probabilistic framework that allows the interpretation of direct, bitwise comparison of Bloom filters to infer similarity and abnormality. Using the similarity interpretation, it is possible to efficiently identify versions of a known object, whereas the notion of abnormality could aid in identifying tampered hash sets. ... Read more

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This volume, divided in two parts, provides a strong foundation for hashing, hash functions, and their application in the context of software development.

While the programming language used in this book is Smalltalk, the information provided applies to other programming languages as well.

The first part develops hashing and hash functions from first principles. The behavior characteristics required of hash functions are examined in detail. A thorough description of how hash functions are constructed follows, complete with a rich survey of existing hash functions.

However, existing hash functions are often inappropriate for the task at hand. To address this problem, the second part shows how to build novel hash functions that are both efficient and highly effective for the range of data types that occur in practice.

The book includes over 230 exercises. Together with their answers, provided in an appendix, these exercises further develop the material in both familiar and new ways. ... Read more

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This digital document is an article from Security Management, published by American Society for Industrial Security on June 1, 2001. The length of the article is 3026 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: Hashing Out Encryption Solutions.
Author: John N. Bumgarner
Publication: Security Management (Refereed)
Date: June 1, 2001
Publisher: American Society for Industrial Security
Volume: 45Issue: 6Page: 67

Distributed by Thomson Gale ... Read more

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High Quality Content by WIKIPEDIA articles! In computer science, a locality preserving hashing is a hash function f that maps a point or points in a multidimensional coordinate space to a scalar value, such that if we have three points A, B and C such that |A-B| < |B-C| Rightarrow |f(A) - f(B)| < |f(B) - f(C)|. , In other words, these are hash functions where the relative distance between the input values is preserved in the relative distance between of the output hash values; input values that are closer to each other will produce output hash values that are closer to each other. This is in contrast to cryptographic hash functions and checksums, which are designed to have maximum output difference between adjacent inputs. ... Read more

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High Quality Content by WIKIPEDIA articles! Locality Sensitive Hashing (LSH) is a method of performing probabilistic dimension reduction of high dimensional data. The basic idea is to hash the input items so that similar items are mapped to the same buckets with high probability (the number of buckets being much smaller than the universe of possible input items). ... Read more

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High Quality Content by WIKIPEDIA articles! Zobrist hashing (also referred to as Zobrist keys or Zobrist signatures ) is a hash function construction used in computer programs that play abstract board games, such as chess and Go, to implement transposition tables, a special kind of hash table that is indexed by a board position. Zobrist hashing is named for its inventor, Albert Zobrist.Zobrist hashing starts by randomly generating bitstrings for each possible element of a board game. Given a certain board position, it breaks up the board into independent components, finds out what state each component is in, and combines the bitstrings representing those elements together using bitwise XOR. If the bitstrings are long enough, different board positions will almost certainly hash to different values; however longer bitstrings require proportionally more computer resources to manipulate. Many game engines store only the hash values in the transposition table, omitting the position information itself entirely to reduce memory usage, and assuming that hash collisions will not occur, or will not greatly influence the results of the table if they do. ... Read more

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Recent advances in computer technology result in a steadily increasing amount of data. For more than 50 years, hash tables are used in computer science to build data structures that allow efficient search operations. Cuckoo hashing is a recently developed algorithm with very interesting worst case properties, that is analysed in detail in this book. The data structure is closely related to a special kind of random graph. Hence, it is possible to analyse its properties by studying the structure of these graphs. The calculations are based on generating functions and asymptotic methods. Although many calculations require detailed knowledge of advanced mathematics, some familiarity with data structures is sufficient to understand and apply the achieved results. ... Read more

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Les achats comprennent une adhésion à l'essai gratuite au club de livres de l'éditeur, dans lequel vous pouvez choisir parmi plus d'un million d'ouvrages, sans frais. Le livre consiste d'articles Wikipedia sur : Machine à Vecteurs de Support, Analyse Discriminante Linéaire, Locality Sensitive Hashing, Classifieur Linéaire, Perceptron, Algorithme Id3, Boosting, Adaboost, Rankboost, Algorithme C4.5, Méthode Des K Plus Proches Voisins, Algorithme Cart. Non illustré. Mises à jour gratuites en ligne. Extrait : Les machines à vecteurs de support ou séparateurs à vaste marge (en anglais Support Vector Machine, SVM) sont un ensemble de techniques d'apprentissage supervisé destinées à résoudre des problèmes de discrimination et de régression. Les SVM sont une généralisation des classifieurs linéaires. Les SVM ont été développés dans les années 1990 à partir des considérations théoriques de Vladimir Vapnik sur le développement d'une théorie statistique de l'apprentissage : la Théorie de Vapnik-Chervonenkis. Les SVM ont rapidement été adoptés pour leur capacité à travailler avec des données de grandes dimensions, le faible nombre d'hyper paramètres, le fait qu'ils soient bien fondés théoriquement, et leurs bons résultats en pratique. Les SVM ont été appliqués à de très nombreux domaines (bio-informatique, recherche d'information, vision par ordinateur, finance...). Selon les données, la performance des machines à vecteurs de support est de même ordre, ou même supérieure, à celle d'un réseau de neurones ou d'un modèle de mixture gaussienne. Les séparateurs à vastes marges reposent sur deux idées clés : la notion de marge maximale et la notion de fonction noyau. Ces deux notions existaient depuis plusieurs années avant qu'elles ne soient mises en commun pour construire les SVM. L'idée des hyperplans à marge maximale a été explorée dès 1963 par Vladimir Vapnik et A. Lerner, et en 1973 par Richard Duda et Peter Hart dans leur livre Pattern C...http://booksllc.net/?l=fr ... Read more