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Random self-reducibility

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23:) is the rule that a good algorithm for the average case implies a good algorithm for the worst case. RSR is the ability to solve all instances of a problem by solving a large fraction of the instances. 240: 675:
s and repeating the above procedure many times, and only providing the majority winner as an answer, we can drive the error rate down very low.
243:
schemes (where a weak private device uses a strong public device without revealing its data) are easily exemplified by random self-reductions.
489:
is random self-reducible. The discussion below considers the case where the matrix entries are drawn from a finite field
158:
is as hard on average as it is in the worst case. This approach contains two key restrictions. First the generation of
302:| is the input size), then there is a randomized polynomial time algorithm for discrete logarithm for all inputs. 766: 466: 256: 236: 216:) can use randomization to ensure that privacy. In fact, the only provably secure cryptographic system (the 32: 231:
utilizes the fact that certain number-theoretic functions are randomly self-reducible. This includes
633: 232: 426: 264: 125: 671:
If we do so, we run the risk of being wrong 1/3 of the time, but by picking multiple random
689: 8: 132:
is the same (within polynomial factors) as the worst-case randomized complexity of 
252: 91:) can be computed in polynomial time, given the coin-toss sequence from the mapping, 747: 462: 260: 457:. Calculating the permanent of a matrix is a difficult computational task— 600:) of them. Then with probability of approximately two-thirds, we can calculate 117:). Therefore, taking the average with respect to the induced distribution on 760: 477:) for most matrices implies the existence of a random program that computes 228: 217: 146:
is distributed uniformly over the entire set of elements in the domain of
685: 700: 61:). In a random self-reduction, an arbitrary worst-case instance 47: 573:
Suppose we know a program that computes the correct value of
389:, and its logarithm can be computed with probability 1/poly( 212:
Problems that require some privacy in the data (typically
207: 190:) is known. Second, it is not necessary that the points 500:, and where all arithmetic is performed in that field. 42:
can be reduced in polynomial time to the evaluation of
534:, by composing those linear functions with the degree 139:
One special case of note is when each random instance
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of a matrix are each random self-reducible problems.
57:, then it is self-reducible (this is also known as a 688:
problem is non-adaptively random self-reducible the
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inversion problem, and the problem of computing the
624:+ 1 values, we can solve for the coefficients of 417:|) and the discrete logarithm is self-reducible. 758: 750:On the Random-self-reducibility of Complete Sets 286:|. If a deterministic polynomial time algorithm 172:is performed non-adaptively. This means that 290:computes the discrete logarithm for a 1/poly( 485:) for all matrices. This demonstrates that 220:) has its security relying totally on the 596:---specifically, 1 − 1/(3 733:M. Abadi, J. Feigenbaum, and J. Kilian, 538:multivariate polynomial that calculates 420: 224:of the key data supplied to the system. 449:is a multivariate polynomial of degree 365:to be distributed uniformly on {0,...,| 69:is mapped to a random set of instances 759: 522:. Since all the entries of any matrix 208:Application in cryptographic protocols 270: 735:On Hiding Information from an Oracle 703:problem is random self-reducible in 469:). Moreover, the ability to compute 59:non-adaptive uniform self-reduction 13: 14: 778: 566:(0) is equal to the permanent of 377:is also distributed uniformly on 294:) fraction of all inputs (where 678: 393:) in polynomial time. Then log 429:of a matrix, it is clear that 237:pseudorandom number generation 1: 737:, J. Comput. Syst. Sci., 1989 727: 369:| − 1}, then 257:quadratic residuosity problem 235:and cryptographically strong 26: 425:Given the definition of the 7: 748:J. Feigenbaum, L. Fortnow, 589:matrices with entries from 246: 10: 783: 204:be uniformly distributed. 515:matrix with entries from 620:+ 1. Once we have those 546:) we get another degree 530:are linear functions of 233:probabilistic encryption 83:. This is done so that 38:evaluating any instance 17:Random self-reducibility 660:(0), which is equal to 656:) exactly, we evaluate 278:: Given a cyclic group 150:that have a length of | 126:average-case complexity 742:Around the PCP Theorem 333:, the discrete log of 214:cryptographic problems 767:Randomized algorithms 554:, which we will call 461:has been shown to be 421:Permanent of a matrix 690:polynomial hierarchy 453:over the entries in 385:is independent of 309:of a cyclic group 305:Given a generator 271:Discrete logarithm 253:discrete logarithm 325:| }, and an 179:is picked before 154:|. In this case 65:in the domain of 774: 648:). Once we know 782: 781: 777: 776: 775: 773: 772: 771: 757: 756: 730: 722: 718: 695: 681: 636:(remember that 594: 520: 496:for some prime 494: 423: 405: 396: 341:is the integer 273: 249: 241:instance-hiding 210: 202: 196: 189: 178: 170: 164: 144: 122: 115: 105: 81: 75: 55: 46:on one or more 29: 12: 11: 5: 780: 770: 769: 755: 754: 745: 738: 729: 726: 725: 724: 720: 716: 697: 693: 692:collapses to Σ 680: 677: 592: 550:polynomial on 518: 492: 422: 419: 401: 394: 272: 269: 248: 245: 209: 206: 200: 194: 187: 176: 168: 162: 142: 120: 113: 103: 79: 73: 53: 28: 25: 9: 6: 4: 3: 2: 779: 768: 765: 764: 762: 753: 751: 746: 743: 739: 736: 732: 731: 714: 710: 706: 702: 698: 691: 687: 683: 682: 676: 674: 669: 667: 663: 659: 655: 651: 647: 644:) has degree 643: 639: 635: 634:interpolation 631: 627: 623: 619: 615: 611: 607: 603: 599: 595: 588: 584: 580: 576: 571: 569: 565: 561: 557: 553: 549: 545: 541: 537: 533: 529: 525: 521: 514: 510: 506: 501: 499: 495: 488: 484: 480: 476: 472: 468: 464: 460: 456: 452: 448: 444: 440: 436: 432: 428: 418: 416: 412: 408: 404: 399: 392: 388: 384: 381:. Therefore 380: 376: 372: 368: 364: 360: 356: 352: 348: 344: 340: 336: 332: 328: 324: 320: 316: 312: 308: 303: 301: 297: 293: 289: 285: 281: 277: 268: 266: 262: 258: 255:problem, the 254: 244: 242: 238: 234: 230: 227:The field of 225: 223: 219: 215: 205: 203: 193: 186: 182: 175: 171: 161: 157: 153: 149: 145: 137: 135: 131: 127: 123: 116: 109: 102: 98: 94: 90: 86: 82: 72: 68: 64: 60: 56: 49: 45: 41: 37: 34: 24: 22: 18: 749: 741: 734: 712: 708: 704: 679:Consequences 672: 670: 665: 661: 657: 653: 649: 645: 641: 637: 629: 625: 621: 617: 613: 609: 605: 601: 597: 590: 586: 582: 578: 574: 572: 567: 563: 562:). Clearly, 559: 555: 551: 547: 543: 539: 535: 531: 527: 523: 516: 512: 508: 507:be a random 504: 502: 497: 490: 486: 482: 478: 474: 470: 458: 454: 450: 446: 442: 438: 434: 430: 424: 414: 410: 406: 402: 397: 390: 386: 382: 378: 374: 370: 366: 362: 358: 354: 350: 346: 342: 338: 337:to the base 334: 330: 326: 322: 318: 314: 310: 306: 304: 299: 295: 291: 287: 283: 279: 275: 274: 250: 229:cryptography 226: 221: 218:one-time pad 213: 211: 198: 191: 184: 180: 173: 166: 159: 155: 151: 147: 140: 138: 133: 129: 118: 111: 107: 100: 96: 92: 88: 84: 77: 70: 66: 62: 58: 51: 43: 39: 35: 30: 20: 16: 15: 686:NP-complete 581:) for most 463:#P-complete 413:(mod | 740:S. Arora, 728:References 616:= 1,2,..., 437:) for any 222:randomness 50:instances 27:Definition 701:CoNP-hard 427:permanent 313:= {  282:of size | 265:permanent 239:. Also, 31:If for a 761:Category 715:) then Σ 632:) using 361:. Take 353:|) with 247:Examples 106:), ..., 33:function 445:matrix 298:= log | 276:Theorem 197:, ..., 165:, ..., 76:, ..., 752:, 1991 744:, 1996 684:If an 612:) for 349:< | 321:< | 317:| 0 ≤ 259:, the 124:, the 95:, and 48:random 707:(log 699:If a 467:proof 400:≡ log 345:(0 ≤ 662:PERM 602:PERM 585:-by- 575:PERM 540:PERM 511:-by- 503:Let 487:PERM 479:PERM 471:PERM 459:PERM 441:-by- 431:PERM 251:The 719:= Π 668:). 261:RSA 128:of 21:RSR 763:: 610:kX 608:+ 570:. 528:kX 526:+ 409:- 407:xg 383:xg 373:= 371:xg 357:= 329:∈ 136:. 723:. 721:2 717:2 713:n 711:/ 709:n 705:O 696:. 694:3 673:X 666:M 664:( 658:p 654:k 652:( 650:p 646:n 642:k 640:( 638:p 630:k 628:( 626:p 622:n 618:n 614:k 606:M 604:( 598:n 593:p 591:F 587:n 583:n 579:A 577:( 568:M 564:p 560:k 558:( 556:p 552:k 548:n 544:M 542:( 536:n 532:k 524:M 519:p 517:F 513:n 509:n 505:X 498:p 493:p 491:F 483:M 481:( 475:M 473:( 465:( 455:M 451:n 447:M 443:n 439:n 435:M 433:( 415:G 411:B 403:g 398:x 395:g 391:n 387:x 379:G 375:g 367:G 363:B 359:g 355:x 351:G 347:k 343:k 339:g 335:x 331:G 327:x 323:G 319:i 315:g 311:G 307:g 300:G 296:n 292:n 288:A 284:G 280:G 201:k 199:y 195:1 192:y 188:1 185:y 183:( 181:f 177:2 174:y 169:k 167:y 163:1 160:y 156:f 152:x 148:f 143:i 141:y 134:f 130:f 121:i 119:y 114:k 112:y 110:( 108:f 104:1 101:y 99:( 97:f 93:x 89:x 87:( 85:f 80:k 78:y 74:1 71:y 67:f 63:x 54:i 52:y 44:f 40:x 36:f 19:(

Index

function
random
average-case complexity
one-time pad
cryptography
probabilistic encryption
pseudorandom number generation
instance-hiding
discrete logarithm
quadratic residuosity problem
RSA
permanent
permanent
#P-complete
proof
interpolation
NP-complete
polynomial hierarchy
CoNP-hard
J. Feigenbaum, L. Fortnow, On the Random-self-reducibility of Complete Sets, 1991
Category
Randomized algorithms

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