Modern Cryptography, Probabilistic Proofs and Pseudorandomness
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On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs*
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs* Benny Applebaum† Eyal Golombek* Abstract We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, CV , that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communica- tion from the prover by R−F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2R−F . Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero- knowledge holds against a “semi-malicious” verifier that maliciously selects its random tape and then plays honestly. -
LINEAR ALGEBRA METHODS in COMBINATORICS László Babai
LINEAR ALGEBRA METHODS IN COMBINATORICS L´aszl´oBabai and P´eterFrankl Version 2.1∗ March 2020 ||||| ∗ Slight update of Version 2, 1992. ||||||||||||||||||||||| 1 c L´aszl´oBabai and P´eterFrankl. 1988, 1992, 2020. Preface Due perhaps to a recognition of the wide applicability of their elementary concepts and techniques, both combinatorics and linear algebra have gained increased representation in college mathematics curricula in recent decades. The combinatorial nature of the determinant expansion (and the related difficulty in teaching it) may hint at the plausibility of some link between the two areas. A more profound connection, the use of determinants in combinatorial enumeration goes back at least to the work of Kirchhoff in the middle of the 19th century on counting spanning trees in an electrical network. It is much less known, however, that quite apart from the theory of determinants, the elements of the theory of linear spaces has found striking applications to the theory of families of finite sets. With a mere knowledge of the concept of linear independence, unexpected connections can be made between algebra and combinatorics, thus greatly enhancing the impact of each subject on the student's perception of beauty and sense of coherence in mathematics. If these adjectives seem inflated, the reader is kindly invited to open the first chapter of the book, read the first page to the point where the first result is stated (\No more than 32 clubs can be formed in Oddtown"), and try to prove it before reading on. (The effect would, of course, be magnified if the title of this volume did not give away where to look for clues.) What we have said so far may suggest that the best place to present this material is a mathematics enhancement program for motivated high school students. -
Lecture 8: Key Derivation, Protecting Passwords, Slow Hashes, Merkle Trees
Lecture 8: Key derivation, protecting passwords, slow hashes, Merkle trees Boaz Barak Last lecture we saw the notion of cryptographic hash functions which are functions that behave like a random function, even in settings (unlike that of standard PRFs) where the adversary has access to the key that allows them to evaluate the hash function. Hash functions have found a variety of uses in cryptography, and in this lecture we survey some of their other applications. In some of these cases, we only need the relatively mild and well-defined property of collision resistance while in others we only know how to analyze security under the stronger (and not precisely well defined) random oracle heuristic. Keys from passwords We have seen great cryptographic tools, including PRFs, MACs, and CCA secure encryptions, that Alice and Bob can use when they share a cryptographic key of 128 bits or so. But unfortunately, many of the current users of cryptography are humans which, generally speaking, have extremely faulty memory capacity for storing large numbers. There are 628 ≈ 248 ways to select a password of 8 upper and lower case letters + numbers, but some letter/numbers combinations end up being chosen much more frequently than others. Due to several large scale hacks, very large databases of passwords have been made public, and one estimate is that the most frequent 10, 000 ≈ 214 passwords account for more than 90% of the passwords chosen. If we choose a password at random from some set D then the entropy of the password is simply log |D|. Estimating the entropy of real life passwords is rather difficult. -
Foundations of Cryptography – a Primer Oded Goldreich
Foundations of Cryptography –APrimer Foundations of Cryptography –APrimer Oded Goldreich Department of Computer Science Weizmann Institute of Science Rehovot Israel [email protected] Boston – Delft Foundations and TrendsR in Theoretical Computer Science Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 USA Tel. +1 781 871 0245 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 A Cataloging-in-Publication record is available from the Library of Congress Printed on acid-free paper ISBN: 1-933019-02-6; ISSNs: Paper version 1551-305X; Electronic version 1551-3068 c 2005 O. Goldreich All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. now Publishers Inc. has an exclusive license to publish this mate- rial worldwide. Permission to use this content must be obtained from the copyright license holder. Please apply to now Publishers, PO Box 179, 2600 AD Delft, The Netherlands, www.nowpublishers.com; e-mail: [email protected] Contents 1 Introduction and Preliminaries 1 1.1 Introduction 1 1.2 Preliminaries 7 IBasicTools 10 2 Computational Difficulty and One-way Functions 13 2.1 One-way functions 14 2.2 Hard-core predicates 18 3 Pseudorandomness 23 3.1 Computational indistinguishability 24 3.2 Pseudorandom generators -
Pseudorandomness and Computational Difficulty
PSEUDORANDOMNESS AND COMPUTATIONAL DIFFICULTY RESEARCH THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF SCIENCE HUGO KRAWCZYK SUBMITTED TOTHE SENATEOFTHE TECHNION —ISRAEL INSTITUTE OF TECHNOLOGY SHVAT 5750 HAIFAFEBRUARY 1990 Amis padres, AurorayBernardo, aquienes debo mi camino. ALeonor,compan˜era inseparable. ALiat, dulzuraycontinuidad. This research was carried out in the Computer Science Department under the supervision of Pro- fessor Oded Goldreich. To Oded, my deep gratitude for being both teacher and friend. Forthe innumerable hoursofstimulating conversations. Forhis invaluable encouragement and support. Iamgrateful to all the people who have contributed to this research and given me their support and help throughout these years. Special thanks to Benny Chor,Shafi Goldwasser,Eyal Kushilevitz, Jacob Ukel- son and Avi Wigderson. ToMikeLuby thanks for his collaboration in the work presented in chapter 2. Ialso wish to thank the Wolf Foundation for their generous financial support. Finally,thanks to Leonor,without whom all this would not have been possible. Table of Contents Abstract ............................. 1 1. Introduction ........................... 3 1.1 Sufficient Conditions for the Existence of Pseudorandom Generators ........ 4 1.2 The Existence of Sparse Pseudorandom Distributions . ............ 5 1.3 The Predictability of Congruential Generators ............... 6 1.4 The Composition of Zero-Knowledge Interactive-Proofs . ........... 8 2. On the Existence of Pseudorandom Generators ............... 10 2.1. Introduction .......................... 10 2.2. The Construction of Pseudorandom Generators .............. 13 2.3. Applications: Pseudorandom Generators based on Particular Intractability Assump- tions . ............................ 22 3. Sparse Pseudorandom Distributions ................... 25 3.1. Introduction .......................... 25 3.2. Preliminaries ......................... 25 3.3. The Existence of Sparse Pseudorandom Ensembles ............. 27 3.4. -
The Complexity Zoo
The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/. -
Algorithms and NP
Algorithms Complexity P vs NP Algorithms and NP G. Carl Evans University of Illinois Summer 2019 Algorithms and NP Algorithms Complexity P vs NP Review Algorithms Model algorithm complexity in terms of how much the cost increases as the input/parameter size increases In characterizing algorithm computational complexity, we care about Large inputs Dominant terms p 1 log n n n n log n n2 n3 2n 3n n! Algorithms and NP Algorithms Complexity P vs NP Closest Pair 01 closestpair(p1;:::; pn) : array of 2D points) 02 best1 = p1 03 best2 = p2 04 bestdist = dist(p1,p2) 05 for i = 1 to n 06 for j = 1 to n 07 newdist = dist(pi ,pj ) 08 if (i 6= j and newdist < bestdist) 09 best1 = pi 10 best2 = pj 11 bestdist = newdist 12 return (best1, best2) Algorithms and NP Algorithms Complexity P vs NP Mergesort 01 merge(L1,L2: sorted lists of real numbers) 02 if (L1 is empty and L2 is empty) 03 return emptylist 04 else if (L2 is empty or head(L1) <= head(L2)) 05 return cons(head(L1),merge(rest(L1),L2)) 06 else 07 return cons(head(L2),merge(L1,rest(L2))) 01 mergesort(L = a1; a2;:::; an: list of real numbers) 02 if (n = 1) then return L 03 else 04 m = bn=2c 05 L1 = (a1; a2;:::; am) 06 L2 = (am+1; am+2;:::; an) 07 return merge(mergesort(L1),mergesort(L2)) Algorithms and NP Algorithms Complexity P vs NP Find End 01 findend(A: array of numbers) 02 mylen = length(A) 03 if (mylen < 2) error 04 else if (A[0] = 0) error 05 else if (A[mylen-1] 6= 0) return mylen-1 06 else return findendrec(A,0,mylen-1) 11 findendrec(A, bottom, top: positive integers) 12 if (top -
Lecture 11 — October 16, 2012 1 Overview 2
6.841: Advanced Complexity Theory Fall 2012 Lecture 11 | October 16, 2012 Prof. Dana Moshkovitz Scribe: Hyuk Jun Kweon 1 Overview In the previous lecture, there was a question about whether or not true randomness exists in the universe. This question is still seriously debated by many physicists and philosophers. However, even if true randomness does not exist, it is possible to use pseudorandomness for practical purposes. In any case, randomization is a powerful tool in algorithms design, sometimes leading to more elegant or faster ways of solving problems than known deterministic methods. This naturally leads one to wonder whether randomization can solve problems outside of P, de- terministic polynomial time. One way of viewing randomized algorithms is that for each possible setting of the random bits used, a different strategy is pursued by the algorithm. A property of randomized algorithms that succeed with bounded error is that the vast majority of strategies suc- ceed. With error reduction, all but exponentially many strategies will succeed. A major theme of theoretical computer science over the last 20 years has been to turn algorithms with this property into algorithms that deterministically find a winning strategy. From the outset, this seems like a daunting task { without randomness, how can one efficiently search for such strategies? As we'll see in later lectures, this is indeed possible, modulo some hardness assumptions. There are two main objectives in this lecture. The first objective is to introduce some probabilis- tic complexity classes, by bounding time or space. These classes are probabilistic analogues of deterministic complexity classes P and L. -
On Notions of Security for Deterministic Encryption, and Efficient Constructions Without Random Oracles
A preliminary version of this paper appears in Advances in Cryptology - CRYPTO 2008, 28th Annual International Cryptology Conference, D. Wagner ed., LNCS, Springer, 2008. This is the full version. On Notions of Security for Deterministic Encryption, and Efficient Constructions without Random Oracles Alexandra Boldyreva∗ Serge Fehr† Adam O’Neill∗ Abstract The study of deterministic public-key encryption was initiated by Bellare et al. (CRYPTO ’07), who provided the “strongest possible” notion of security for this primitive (called PRIV) and con- structions in the random oracle (RO) model. We focus on constructing efficient deterministic encryption schemes without random oracles. To do so, we propose a slightly weaker notion of security, saying that no partial information about encrypted messages should be leaked as long as each message is a-priori hard-to-guess given the others (while PRIV did not have the latter restriction). Nevertheless, we argue that this version seems adequate for certain practical applica- tions. We show equivalence of this definition to single-message and indistinguishability-based ones, which are easier to work with. Then we give general constructions of both chosen-plaintext (CPA) and chosen-ciphertext-attack (CCA) secure deterministic encryption schemes, as well as efficient instantiations of them under standard number-theoretic assumptions. Our constructions build on the recently-introduced framework of Peikert and Waters (STOC ’08) for constructing CCA-secure probabilistic encryption schemes, extending it to the deterministic-encryption setting and yielding some improvements to their original results as well. Keywords: Public-key encryption, deterministic encryption, lossy trapdoor functions, leftover hash lemma, standard model. ∗ College of Computing, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332, USA. -
On the Foundations of Cryptography∗
On the Foundations of Cryptography∗ Oded Goldreich Department of Computer Science Weizmann Institute of Science Rehovot, Israel. [email protected] May 6, 2019 Abstract We survey the main paradigms, approaches and techniques used to conceptualize, define and provide solutions to natural cryptographic problems. We start by presenting some of the central tools used in cryptography; that is, computational difficulty (in the form of one-way functions), pseudorandomness, and zero-knowledge proofs. Based on these tools, we turn to the treatment of basic cryptographic applications such as encryption and signature schemes as well as the design of general secure cryptographic protocols. Our presentation assumes basic knowledge of algorithms, probability theory and complexity theory, but nothing beyond this. Keywords: Cryptography, Theory of Computation. ∗This revision of the primer [59] will appear as Chapter 17 in an ACM book celebrating the work of Goldwasser and Micali. 1 Contents 1 Introduction and Preliminaries 1 1.1 Introduction.................................... ............... 1 1.2 Preliminaries ................................... ............... 4 I Basic Tools 6 2 Computational Difficulty and One-Way Functions 6 2.1 One-WayFunctions................................ ............... 6 2.2 Hard-CorePredicates . .. .. .. .. .. .. .. ................ 9 3 Pseudorandomness 11 3.1 Computational Indistinguishability . ....................... 11 3.2 PseudorandomGenerators. ................. 12 3.3 PseudorandomFunctions . ............... -
User's Guide for Complexity: a LATEX Package, Version 0.80
User’s Guide for complexity: a LATEX package, Version 0.80 Chris Bourke April 12, 2007 Contents 1 Introduction 2 1.1 What is complexity? ......................... 2 1.2 Why a complexity package? ..................... 2 2 Installation 2 3 Package Options 3 3.1 Mode Options .............................. 3 3.2 Font Options .............................. 4 3.2.1 The small Option ....................... 4 4 Using the Package 6 4.1 Overridden Commands ......................... 6 4.2 Special Commands ........................... 6 4.3 Function Commands .......................... 6 4.4 Language Commands .......................... 7 4.5 Complete List of Class Commands .................. 8 5 Customization 15 5.1 Class Commands ............................ 15 1 5.2 Language Commands .......................... 16 5.3 Function Commands .......................... 17 6 Extended Example 17 7 Feedback 18 7.1 Acknowledgements ........................... 19 1 Introduction 1.1 What is complexity? complexity is a LATEX package that typesets computational complexity classes such as P (deterministic polynomial time) and NP (nondeterministic polynomial time) as well as sets (languages) such as SAT (satisfiability). In all, over 350 commands are defined for helping you to typeset Computational Complexity con- structs. 1.2 Why a complexity package? A better question is why not? Complexity theory is a more recent, though mature area of Theoretical Computer Science. Each researcher seems to have his or her own preferences as to how to typeset Complexity Classes and has built up their own personal LATEX commands file. This can be frustrating, to say the least, when it comes to collaborations or when one has to go through an entire series of files changing commands for compatibility or to get exactly the look they want (or what may be required). -
A Note on Perfect Correctness by Derandomization⋆
A Note on Perfect Correctness by Derandomization? Nir Bitansky and Vinod Vaikuntanathan MIT Abstract. We show a general compiler that transforms a large class of erroneous cryptographic schemes (such as public-key encryption, indis- tinguishability obfuscation, and secure multiparty computation schemes) into perfectly correct ones. The transformation works for schemes that are correct on all inputs with probability noticeably larger than half, and are secure under parallel repetition. We assume the existence of one-way functions and of functions with deterministic (uniform) time complexity 2O(n) and non-deterministic circuit complexity 2Ω(n). Our transformation complements previous results that showed how public- key encryption and indistinguishability obfuscation that err on a notice- able fraction of inputs can be turned into ones that for all inputs are often correct. The technique relies on the idea of \reverse randomization" [Naor, Crypto 1989] and on Nisan-Wigderson style derandomization, previously used in cryptography to remove interaction from witness-indistinguishable proofs and commitment schemes [Barak, Ong and Vadhan, Crypto 2003]. 1 Introduction Randomized algorithms are often faster and simpler than their state-of-the-art deterministic counterparts, yet, by their very nature, they are error-prone. This gap has motivated a rich study of derandomization, where a central avenue has been the design of pseudo-random generators [BM84, Yao82a, NW94] that could offer one universal solution for the problem. This has led to surprising results, intertwining cryptography and complexity theory, and culminating in a deran- domization of BPP under worst-case complexity assumptions, namely, the ex- istence of functions in E = Dtime(2O(n)) with worst-case circuit complexity 2Ω(n) [NW94, IW97].