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Pseudorandomness

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70:, which are both modeled as being truly random processes in the underlying physics. Since these processes are not practical sources of random numbers, pseudorandom numbers are used, which ideally have the unpredictability of a truly random sequence, despite being generated by a deterministic process. 92:
In some cases where it is important for the sequence to be demonstrably unpredictable, physical sources of random numbers have been used, such as radioactive decay, atmospheric electromagnetic noise harvested from a radio tuned between stations, or intermixed timings of
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and repeatable process. Simply put, the problem is that many of the sources of randomness available to humans (such as rolling dice) rely on physical processes not readily available to computer programs.
62:, however, most processes, such as gravitational acceleration, are deterministic, meaning that they always produce the same outcome from the same starting point. Some notable exceptions are 154:
against a class of adversaries if no adversary from the class can distinguish it from the uniform distribution with significant advantage. This notion of pseudorandomness is studied in
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The first attempt to provide researchers with a ready supply of random digits was in 1927, when the Cambridge University Press published a table of 41,600 digits developed by
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pseudorandomness, the theory of efficiently generating objects that "look random" despite being constructed using little or no randomness
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generated numbers by the electronic simulation of a roulette wheel; the results were eventually published in 1955 as
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See especially Chapter 8: Pseudorandom generators, pp. 284–348, and Appendix C.2: Pseudorandomness, pp. 490–493.
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Before modern computing, researchers requiring random numbers would either generate them through various means (
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describes a model of computation with bounded resources and one is interested in designing distributions
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Oded Goldreich. Computational Complexity: A Conceptual Perspective. Cambridge University Press. 2008.
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In RFC 4086, the use of pseudorandom number sequences in cryptography is discussed at length.
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Jonathan Knudson (January 1998). "Javatalk: Horseshoes, hand grenades and random numbers".
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Appearing random but actually being generated by a deterministic, causal process
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applications, where the pattern's unpredictability is a critical feature.
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HotBits: Genuine random numbers, generated by radioactive decay
514:"Connoisseurs of Chaos Offer A Valuable Product: Randomness" 507: 505: 42:
The generation of random numbers has many uses, such as for
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D. Eastlake, 3rd; J. Schiller; S. Crocker (June 2005).
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Using and Creating Cryptographic-Quality Random Numbers
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Foundations and Trends in Theoretical Computer Science
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Cryptographically secure pseudorandom number generator
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with certain properties that are pseudorandom against
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In many applications, the deterministic process is a
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A Million Random Digits with 100,000 Normal Deviates
563: 436:Computational Complexity: A Conceptual Perspective 420:, Volume 2: Seminumerical Algorithms (3rd edition) 314: 290: 270: 241: 689: 569: 29:, despite having been produced by a completely 511: 137: 117:, etc.) or use existing random number tables. 25:sequence of numbers is one that appears to be 532: 669:: CS1 maint: numeric names: authors list ( 455:Vadhan, S. P. (2012). "Pseudorandomness". 647: 550: 432: 690: 629: 454: 349:is often specified as the output of a 393: – Type of mathematical sequence 639:Randomness Requirements for Security 583: 581: 333:In typical applications, the class 13: 409: 14: 714: 578: 476: 591:. RAND Corporation. January 2001 512:George Johnson (June 12, 2001). 369:List of random number generators 422:. Addison-Wesley Professional, 418:The Art of Computer Programming 156:computational complexity theory 612: 603: 544: 526: 439:. Cambridge University Press. 265: 259: 236: 230: 189:} be a class of functions. A 1: 496: 385:Pseudorandom number generator 79:pseudorandom number generator 37: 703:Theoretical computer science 551:Mark Ward (August 9, 2015). 374:Pseudorandom binary sequence 144:theoretical computer science 7: 356: 138:In computational complexity 10: 719: 682:. 220:between the distributions 100: 589:"A Million Random Digits" 433:Goldreich, Oded (2008). 397:Random number generation 391:Low-discrepancy sequence 158:and has applications to 415:Donald E. Knuth (1997) 173:be finite sets and let 351:pseudorandom generator 316: 292: 272: 243: 676:Best Common Practice. 533:S. P. Vadhan (2012). 380:Pseudorandom ensemble 317: 293: 273: 244: 345:. The distribution 324:uniform distribution 322:is sampled from the 306: 282: 271:{\displaystyle f(Y)} 253: 242:{\displaystyle f(X)} 224: 218:statistical distance 27:statistically random 678:Obsoletes RFC  68:quantum measurement 48:Monte Carlo methods 620:"Pseudorandomness" 519:The New York Times 469:10.1561/0400000010 403:Pseudorandom noise 312: 288: 268: 239: 75:computer algorithm 574:. pp. 16–17. 446:978-0-521-88473-0 315:{\displaystyle Y} 291:{\displaystyle X} 64:radioactive decay 710: 698:Pseudorandomness 683: 674: 668: 660: 651: 649:10.17487/RFC4086 633: 627: 626: 624: 616: 610: 607: 601: 600: 598: 596: 585: 576: 575: 567: 561: 560: 548: 542: 541: 535:Pseudorandomness 530: 524: 523: 509: 472: 450: 330:, is at most ε. 321: 319: 318: 313: 298:is sampled from 297: 295: 294: 289: 277: 275: 274: 269: 248: 246: 245: 240: 126:RAND Corporation 718: 717: 713: 712: 711: 709: 708: 707: 688: 687: 686: 662: 661: 652:. BCP 106. 634: 630: 622: 618: 617: 613: 608: 604: 594: 592: 587: 586: 579: 568: 564: 549: 545: 531: 527: 510: 503: 499: 479: 447: 412: 410:Further reading 359: 307: 304: 303: 283: 280: 279: 254: 251: 250: 225: 222: 221: 140: 124:. In 1947, the 115:roulette wheels 103: 44:random sampling 40: 19: 12: 11: 5: 716: 706: 705: 700: 685: 684: 628: 611: 602: 577: 562: 543: 525: 500: 498: 495: 494: 493: 490: 485: 478: 477:External links 475: 474: 473: 463:(1–3): 1–336. 452: 445: 430: 411: 408: 407: 406: 400: 394: 388: 382: 377: 371: 366: 358: 355: 311: 287: 267: 264: 261: 258: 238: 235: 232: 229: 165:Formally, let 139: 136: 122:L.H.C. Tippett 102: 99: 39: 36: 17: 9: 6: 4: 3: 2: 715: 704: 701: 699: 696: 695: 693: 681: 677: 672: 666: 658: 655: 650: 645: 641: 640: 632: 621: 615: 606: 590: 584: 582: 573: 566: 558: 554: 547: 540: 536: 529: 521: 520: 515: 508: 506: 501: 491: 489: 486: 484: 481: 480: 470: 466: 462: 458: 453: 448: 442: 438: 437: 431: 429: 428:0-201-89684-2 425: 421: 419: 414: 413: 404: 401: 398: 395: 392: 389: 386: 383: 381: 378: 375: 372: 370: 367: 364: 361: 360: 354: 352: 348: 344: 340: 336: 331: 329: 325: 309: 301: 285: 262: 256: 233: 227: 219: 215: 211: 208:if for every 207: 203: 199: 195: 192: 188: 184: 180: 176: 172: 168: 163: 161: 157: 153: 149: 145: 135: 133: 132: 127: 123: 118: 116: 112: 108: 98: 96: 90: 88: 84: 80: 76: 71: 69: 65: 61: 57: 53: 49: 45: 35: 32: 31:deterministic 28: 24: 16: 675: 638: 631: 614: 605: 593:. Retrieved 571: 565: 556: 546: 538: 534: 528: 517: 460: 456: 435: 416: 346: 342: 338: 334: 332: 327: 299: 213: 209: 205: 202:pseudorandom 201: 197: 193: 191:distribution 186: 182: 178: 174: 170: 166: 164: 160:cryptography 152:pseudorandom 151: 148:distribution 141: 129: 119: 104: 91: 72: 41: 23:pseudorandom 22: 20: 15: 83:random seed 52:board games 692:Categories 572:Sun Server 497:References 95:keystrokes 38:Background 595:March 30, 77:called a 665:citation 357:See also 278:, where 204:against 87:security 56:gambling 101:History 60:physics 443:  426:  216:, the 58:. 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Index

statistically random
deterministic
random sampling
Monte Carlo methods
board games
gambling
physics
radioactive decay
quantum measurement
computer algorithm
pseudorandom number generator
random seed
security
keystrokes
dice
cards
roulette wheels
L.H.C. Tippett
RAND Corporation
A Million Random Digits with 100,000 Normal Deviates
theoretical computer science
distribution
computational complexity theory
cryptography
distribution
statistical distance
uniform distribution
pseudorandom generator
Cryptographically secure pseudorandom number generator
List of random number generators

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