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Probabilistic Computations: Mild Derandomizatons and Zero-Knowledge Classes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Probabilistic Computations: Mild Derandomizatons and Zero-Knowledge Classes./
Author:	Dixon, Peter.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	88 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412824
ISBN:	9798544277057

Probabilistic Computations: Mild Derandomizatons and Zero-Knowledge Classes.
Dixon, Peter.

Probabilistic Computations: Mild Derandomizatons and Zero-Knowledge Classes. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 88 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Iowa State University, 2021.

This item must not be sold to any third party vendors.

Random algorithms have a unique place in complexity theory as a model of computation that ispotentially more powerful than "normal" algorithms, and is also practical. However, it is still notclear how much more power randomness adds. The primary goal in studying random algorithmsis derandomization - some method to simulate random algorithms without actually using random-ness. While full derandomization is quite difficult, we show some weak derandomization results -one using advice, and one using multi-pseudodeterminism. We show that improving these resultswould have major implications. Finally, we show new containments and oracle separations betweentraditional random classes and zero-knowledge proofs.

ISBN: 9798544277057Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Cryptography

Probabilistic Computations: Mild Derandomizatons and Zero-Knowledge Classes.
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245 1 0 $a Probabilistic Computations: Mild Derandomizatons and Zero-Knowledge Classes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 88 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Aduri, Pavan.
502 $a Thesis (Ph.D.)--Iowa State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Random algorithms have a unique place in complexity theory as a model of computation that ispotentially more powerful than "normal" algorithms, and is also practical. However, it is still notclear how much more power randomness adds. The primary goal in studying random algorithmsis derandomization - some method to simulate random algorithms without actually using random-ness. While full derandomization is quite difficult, we show some weak derandomization results -one using advice, and one using multi-pseudodeterminism. We show that improving these resultswould have major implications. Finally, we show new containments and oracle separations betweentraditional random classes and zero-knowledge proofs.
590 $a School code: 0097.
650 4 $a Computer science. $3 523869
653 $a Cryptography
653 $a Pseudorandomness
653 $a Randomized algorithms
653 $a Theoretical computer science
690 $a 0984
710 2 $a Iowa State University. $b Computer Science. $3 1022280
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0097
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412824