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Cryptographic Hash Function
A CRYPTOGRAPHIC HASH FUNCTION is a special class of hash function that has certain properties which make it suitable for use in cryptography . It is a mathematical algorithm that maps data of arbitrary size to a bit string of a fixed size (a hash function ) which is designed to also be a one-way function , that is, a function which is infeasible to invert. The only way to recreate the input data from an ideal cryptographic hash function's output is to attempt a brute-force search of possible inputs to see if they produce a match, or use a rainbow table of matched hashes. Bruce Schneier
Bruce Schneier
has called one-way hash functions "the workhorses of modern cryptography". The input data is often called the message, and the output (the hash value or hash) is often called the message digest or simply the digest
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Polynomial Time
In computer science , the TIME COMPLEXITY of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input. :226 The time complexity of an algorithm is commonly expressed using big O notation , which excludes coefficients and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n for any n (bigger than some n0), the asymptotic time complexity is O(n3). Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor
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Asymptotic Computational Complexity
In computational complexity theory , ASYMPTOTIC COMPUTATIONAL COMPLEXITY is the usage of asymptotic analysis for the estimation of computational complexity of algorithms and computational problems , commonly associated with the usage of the big O notation . CONTENTS * 1 Scope * 2 Types of algorithms considered * 3 See also * 4 References SCOPEWith respect to computational resources , ASYMPTOTIC TIME COMPLEXITY and ASYMPTOTIC SPACE COMPLEXITY are commonly estimated. Other asymptotically estimated behavior include circuit complexity and various measures of parallel computation , such as the number of (parallel) processors. Since the ground-breaking 1965 paper by Juris Hartmanis and Richard E. Stearns and the 1979 book by Michael Garey and David S. Johnson on NP-completeness , the term "computational complexity " (of algorithms) has become commonly referred to as asymptotic computational complexity
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String (computer Science)
In computer programming , a STRING is traditionally a sequence of characters , either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation). A string is generally understood as a data type and is often implemented as an array data structure of bytes (or words ) that stores a sequence of elements, typically characters, using some character encoding . A string may also denote more general arrays or other sequence (or list ) data types and structures. Depending on programming language and precise data type used, a variable declared to be a string may either cause storage in memory to be statically allocated for a predetermined maximum length or employ dynamic allocation to allow it to hold a variable number of elements. When a string appears literally in source code , it is known as a string literal or an anonymous string
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Exponential Time
In computer science , the TIME COMPLEXITY of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input. :226 The time complexity of an algorithm is commonly expressed using big O notation , which excludes coefficients and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n for any n (bigger than some n0), the asymptotic time complexity is O(n3). Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor
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Cyclic Redundancy Check
A CYCLIC REDUNDANCY CHECK (CRC) is an error-detecting code commonly used in digital networks and storage devices to detect accidental changes to raw data. Blocks of data entering these systems get a short check value attached, based on the remainder of a polynomial division of their contents. On retrieval, the calculation is repeated and, in the event the check values do not match, corrective action can be taken against data corruption. CRCs can be used for error correction, see bitfilters . CRCs are so called because the check (data verification) value is a redundancy (it expands the message without adding information ) and the algorithm is based on cyclic codes . CRCs are popular because they are simple to implement in binary hardware , easy to analyze mathematically, and particularly good at detecting common errors caused by noise in transmission channels. Because the check value has a fixed length, the function that generates it is occasionally used as a hash function
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Computational Complexity Theory
COMPUTATIONAL COMPLEXITY THEORY is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm . A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage
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Adversary (cryptography)
In cryptography , an ADVERSARY (rarely OPPONENT, ENEMY) is a malicious entity whose aim is to prevent the users of the cryptosystem from achieving their goal (primarily privacy, integrity, and availability of data). An adversary's efforts might take the form of attempting to discover secret data, corrupting some of the data in the system, spoofing the identity of a message sender or receiver, or forcing system downtime. Actual adversaries, as opposed to idealized ones, are referred to as attackers. Not surprisingly, the former term predominates in the cryptographic and the latter in the computer security literature. Eve, Mallory, Oscar and Trudy are all adversarial characters widely used in both types of texts. This notion of an adversary helps both intuitive and formal reasoning about cryptosystems by casting security analysis of cryptosystems as a 'game' between the users and a centrally co-ordinated enemy
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CRC32
A CYCLIC REDUNDANCY CHECK (CRC) is an error-detecting code commonly used in digital networks and storage devices to detect accidental changes to raw data. Blocks of data entering these systems get a short check value attached, based on the remainder of a polynomial division of their contents. On retrieval, the calculation is repeated and, in the event the check values do not match, corrective action can be taken against data corruption. CRCs can be used for error correction, see bitfilters . CRCs are so called because the check (data verification) value is a redundancy (it expands the message without adding information ) and the algorithm is based on cyclic codes . CRCs are popular because they are simple to implement in binary hardware , easy to analyze mathematically, and particularly good at detecting common errors caused by noise in transmission channels. Because the check value has a fixed length, the function that generates it is occasionally used as a hash function
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Wired Equivalent Privacy
WIRED EQUIVALENT PRIVACY (WEP) is a security algorithm for IEEE 802.11 wireless networks . Introduced as part of the original 802.11 standard ratified in 1997, its intention was to provide data confidentiality comparable to that of a traditional wired network . WEP, recognizable by its key of 10 or 26 hexadecimal digits (40 or 104 bits ), was at one time widely in use and was often the first security choice presented to users by router configuration tools. In 2003 the Wi-Fi Alliance
Wi-Fi Alliance
announced that WEP had been superseded by Wi-Fi Protected Access
Wi-Fi Protected Access
(WPA). In 2004, with the ratification of the full 802.11i standard (i.e. WPA2 ), the IEEE declared that both WEP-40 and WEP-104 have been deprecated
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Random Function
In probability theory and related fields, a STOCHASTIC or RANDOM PROCESS is a mathematical object usually defined as a collection of random variables . Historically, the random variables were associated with or indexed by a set of numbers, usually viewed as points in time, giving the interpretation of a stochastic process representing numerical values of some system randomly changing over time , such as the growth of a bacterial population, an electrical current fluctuating due to thermal noise , or the movement of a gas molecule . Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. They have applications in many disciplines including sciences such as biology , chemistry , ecology , neuroscience , and physics as well as technology and engineering fields such as image processing , signal processing , information theory , computer science , cryptography and telecommunications
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Concatenation
In formal language theory and computer programming , STRING CONCATENATION is the operation of joining character strings end-to-end. For example, the concatenation of "snow" and "ball" is "snowball". In some but not all formalisations of concatenation theory , also called string theory, string concatenation is a primitive notion . CONTENTS * 1 Syntax * 2 Implementation * 3 Concatenation of sets of strings * 4 Algebraic properties * 5 Applications * 5.1 Audio/telephony * 5.2 Database theory * 6 References SYNTAXIn many programming languages , string concatenation is a binary infix operator . The + (plus) operator is often overloaded to denote concatenation for string arguments: "Hello, " + "World" has the value "Hello, World". In other languages there is a separate operator, particularly to specify implicit type conversion to string, as opposed to more complicated behavior for generic plus. Examples include
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Alice And Bob
ALICE and BOB are fictional characters commonly used as placeholder names in cryptology , as well as science and engineering literature. The Alice and Bob
Alice and Bob
characters were invented by Ron Rivest
Ron Rivest
, Adi Shamir , and Leonard Adleman in their 1978 paper "A method for obtaining digital signatures and public-key cryptosystems." Subsequently, they have become common archetypes in many scientific and engineering fields, such as quantum cryptography , game theory and physics . As the use of Alice and Bob
Alice and Bob
became more popular, additional characters were added, each with a particular meaning
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Authentication
AUTHENTICATION (from Greek : αὐθεντικός authentikos, "real, genuine", from αὐθέντης authentes, "author") is the act of confirming the truth of an attribute of a single piece of data claimed true by an entity. In contrast with identification, which refers to the act of stating or otherwise indicating a claim purportedly attesting to a person or thing's identity, authentication is the process of actually confirming that identity. It might involve confirming the identity of a person by validating their identity documents , verifying the authenticity of a website with a digital certificate , determining the age of an artifact by carbon dating , or ensuring that a product is what its packaging and labeling claim to be. In other words, authentication often involves verifying the validity of at least one form of identification
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Message Integrity
INFORMATION SECURITY, sometimes shortened to INFOSEC, is the practice of preventing unauthorized access, use, disclosure, disruption, modification, inspection, recording or destruction of information . It is a general term that can be used regardless of the form the data may take (e.g., electronic, physical). Information
Information
security's primary focus is the balanced protection of the confidentiality, integrity and availability of data (also known as the CIA triad) while maintaining a focus on efficient policy implementation, all without hampering organization productivity. This is largely achieved through a multi-step risk management process that identifies assets, threat sources, vulnerabilities, potential impacts, and possible controls, followed by assessment of the effectiveness of the risk management plan
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Brute-force Search
In computer science , BRUTE-FORCE SEARCH or EXHAUSTIVE SEARCH, also known as GENERATE AND TEST, is a very general problem-solving technique that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem's statement. A brute-force algorithm to find the divisors of a natural number n would enumerate all integers from 1 to n, and check whether each of them divides n without remainder. A brute-force approach for the eight queens puzzle would examine all possible arrangements of 8 pieces on the 64-square chessboard, and, for each arrangement, check whether each (queen) piece can attack any other. While a brute-force search is simple to implement, and will always find a solution if it exists, its cost is proportional to the number of candidate solutions – which in many practical problems tends to grow very quickly as the size of the problem increases
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