# Appendix E. Glossary

 Asymmetrical EncryptionEncryption using a pair of keys the first encrypts a message that the second decrypts. In the most common protocol, the decryption key is kept secret but the encryption key may be widely revealed. For example, you might publish your encryption or "public" key, which lets anyone encrypt a message that only you can decrypt. The person who first creates the message, of course, has initial access to it, but any third-party without the decryption or "private" key cannot access the message. See Section 2.2.4 for a discussion of cryptographic capabilities. Big-O Notation, ComplexityBig-O notation is a way of describing the governing asymptotic complexity of an algorithm. Often such complexity is described using a capital "O" with an expression on "n" following in parentheses. Textbooks often use a bold letter or a special typeface for the "O". The "O" is originally associated with "order" of complexity. The insight behind big-O notation is that many problems require a calculation time that can be expressed as a formula involving the size of the data set or domain at issue. For the most important complexity orders, constant startup times and even speed multipliers are overpowered by the underlying complexity. For example, suppose that you have an algorithm that takes 100 seconds to initialize some data structures and 10*(N^2) seconds to perform the main calculation. If you have N=4 objects, the total runtime will be 260 seconds; saving that 100 seconds initialization might seem worthwhile, if possible. However, if you also need to deal with N=10 objects, you are looking at 1,100 seconds in total, and the initialization is a minor component. Moreover, you might think it significant to go from 10*(N^2) seconds to only 2*(N^2) seconds say, by using a faster CPU or programming language. Once you consider the 100,100 seconds it will take to calculate for N=100, even the multiplier is not all that important. In particular if you had a better algorithm that took, for example, 50*N seconds (bigger multiplier), you would be a lot better off only needing 50,000 seconds. In noting complexity orders, constants and multipliers are conventionally omitted, leaving only the dominant factor. Compexities one often sees are: ` 0(1) constant 0(log(n)) logarithmic 0((log(n))^c) polylogarithmic 0(n) linear 0(n*log(n)) frequent in sorts and other problems 0(n^2) quadratic 0(n^c) polynomial 0(c^n) exponential (super-polynomial) ` Birthday ParadoxThe name "birthday paradox" comes from the fact surprising to many people that in a room with just 23 people there is a 50 percent chance of two of them sharing a birthday. A naive hunch is often that, since there are 365 days, it should instead take something like 180 people to reach this likelihood. In a broader sense the probability of collision of two events, where N outcomes are possible, reaches 50 percent when approximately sqrt(N) items are collected. This is a concern when you want hashes, random selections, and the like to consist of only distinct values. Cryptographic HashA hash with a strong enough noncollision property that a tamperer cannot produce a false message yielding the same hash as does an authentic message. See Section 2.2.4 for a discussion of cryptographic capabilities. Cyclic Redundancy Check (CRC32)Based on mod 2 polynomial operations, CRC32 produces a 32-bit "fingerprint" of a set of data.See also [Hash] Digital SignaturesA means of proving the authenticity of a message. As with asymmetric encryption, digital signatures involve two keys. The signing key is kept secret, but a published validation key can be used to show that the owner of the signing key used it to authenticate a message. See Section 2.2.4 for a discussion of cryptographic capabilities. HashA short value that is used as a "fingerprint" of a larger collection of data. It should be unlikely that two data sets will yield the same hash value. Hashes can be used to check for data errors, by comparing data to an indicated hash value (mismatch suggests data error). Some hashes have sufficient noncollision properties to be used cryptographically. Idempotent FunctionThe property that applying a function to its return value returns an identical value. That is, if and only if F is idempotent then F(x)==F(F(x)), for every x. In a nod to Chaos Theory, we can observe that if some function defined by finite repetitions of composition with F is idempotent, then F has an attractor that is, if G is idempotent for G=lambda x:F(F(F((x) ...))). This interesting fact is completely unnecessary to understand the rest of this book. ImmutableLiterally, "cannot be changed." Some data collection objects notably tuples and strings, in Python consist of a set of items, and the membership cannot change over the life of the object. In contrast, mutable objects like lists and dictionaries can continue to be the same object, while changing their membership. Since you generally access objects in Python via names (and index positions), it is sometimes easy to confuse the mere name which can be used at different times to point to different objects with the underlying objects. For example, a pattern with tuples like the one below is common: ` >>> tup = (1,2,3) >>> id(tup) 248684 >>> tup = tup+(4,5,6) >>> tup (1, 2, 3, 4, 5, 6) >>> id(tup) 912076 ` Even though the name tup is re-bound during the run, the identity of the bound object changes. Moreover, creating a tuple with the same objects later produces the same identity: ` >>> tup2 = (1,2,3) >>> id(tup2) 248684 ` Immutable objects are particularly useful as dictionary keys, since they will continue to hash the same way over program run. However, "hashability" is a stricter constraint than immutability it is necessary that every member of an immutable object itself be (recursively) immutable in order to be hashable. MutableLiterally, "can be changed." Data collection objects like lists, dictionaries, and arrays from the array module are mutable. The identity of these objects stays the same, even as their membership changes. Mutable objects are not (usually) suitable as dictionary keys, however. Conceptually, lists are often used to hold records of a data collection, where tuples are used to hold fields within a record. The insight underlying this distinction is that if a record contained different field data, it would not be the same record. But individual self-identical records can be added or subtracted from a collection, depending on outside events and purposes. Public-key EncryptionSee [Assymmetrical Encryption] Referential TransparencyThe property of a function or block construct such that it will produce the same value every time it is called with the same arguments. Mathematical functions are referentially transparent, by definition, but functions whose results depend on global state, external context, or local mutable values are referentially opaque. Shared-key EncryptionSee [Symmetrical Encryption] Structured Text DatabaseA text file that is used to encode multiple records of data, each record composed of the same fields. Records and fields are also often called rows and columns, respectively. A structured text database might be any textual format that contains little or no explicit markup; the most common variants are delimited files and fixed-width files, both widely used on mainframes and elsewhere. Most of the time, structured text databases are line oriented, with one conceptual record per line; but at times, devices like indentation are used to indicate dependent subrecords. Symmetrical EncryptionEncryption using a single "key" that must be shared between parties. See Section 2.2.4 for a discussion of cryptographic capabilities.

Text Processing in Python
ISBN: 0321112547
EAN: 2147483647
Year: 2005
Pages: 59
Authors: David Mertz

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