Miller-Rabin Test

Total Page:16

File Type:pdf, Size:1020Kb

Miller-Rabin Test THE MILLER{RABIN TEST KEITH CONRAD 1. Introduction The Miller{Rabin test is the most widely used probabilistic primality test. For odd composite n > 1 over 75% of numbers from to 2 to n − 1 are witnesses in the Miller{Rabin test for n. We will describe the test, prove the 75% lower bound (an improvement on the Solovay{Strassen test), and in an appendix use the main idea in the test to show factoring n into primes and computing '(n) are similar computational tasks. 2. The Miller{Rabin test The Fermat and Solovay{Strassen tests are each based on translating a congruence mod- ulo prime numbers, either Fermat's little theorem or Euler's congruence, over to the setting of composite numbers and hoping to make it fail there. The Miller{Rabin test uses a similar idea, but involves a system of congruences. For an odd integer n > 1, factor out the largest power of 2 from n − 1, say n − 1 = 2ek where e ≥ 1 and k is odd. This meaning for e and k will be used throughout. The polynomial xn−1 − 1 = x2ek − 1 can be factored repeatedly as often as we have powers of 2 in the exponent: e e−1 x2 k − 1 = (x2 k)2 − 1 e−1 e−1 = (x2 k − 1)((x2 k + 1) e−2 e−2 e−1 = (x2 k − 1)(x2 k + 1)((x2 k + 1) . e−1 = (xk − 1)(xk + 1)(x2k + 1)(x4k + 1) ··· (x2 k + 1): If n is prime and 1 ≤ a ≤ n − 1 then an−1 − 1 ≡ 0 mod n by Fermat's little theorem, so using the above factorization we have e−1 (ak − 1)(ak + 1)(a2k + 1)(a4k + 1) ··· (a2 k + 1) ≡ 0 mod n: When n is prime one of these factors must be 0 mod n, so i (2.1) ak ≡ 1 mod n or a2 k ≡ −1 mod n for some i 2 f0; : : : ; e − 1g: Example 2.1. If n = 13 then n − 1 = 4 · 3, so e = 2, k = 3, and (2.1) says a3 ≡ 1 mod n or a3 ≡ −1 mod n or a6 ≡ −1 mod n for each a from 1 to 12. Example 2.2. If n = 41 then n − 1 = 8 · 5, so e = 3, k = 5, and (2.1) says a7 ≡ 1 mod n or one of a5; a10, or a20 is congruent to −1 mod n for each a from 1 to 40. The congruences in (2.1) make sense for all odd n > 1, prime or not. Their simultaneous failure for some a in f1; : : : ; n − 1g will lead to a primality test. 1 2 KEITH CONRAD Definition 2.3. For odd n > 1, write n − 1 = 2ek with k odd and pick a 2 f1; : : : ; n − 1g. We say a is a Miller{Rabin witness for n if all of the congruences in (2.1) are false: i ak 6≡ 1 mod n and a2 k 6≡ −1 mod n for all i 2 f0; : : : ; e − 1g: We say a is a Miller{Rabin nonwitness for n (and n is called a strong pseudoprime to the base a) if one of the congruences in (2.1) is true: i ak ≡ 1 mod n or a2 k ≡ −1 mod n for some i 2 f0; : : : ; e − 1g: As in the Fermat and Solovay{Strassen tests, we are using the term \witness" to mean a number that proves n is composite. An odd prime has no Miller{Rabin witnesses, so when n has a Miller{Rabin witness it must be composite. In the definition of a Miller{Rabin witness, the case i = 0 says ak 6≡ −1 mod n, so another way of describing a witness is ak 6≡ ±1 mod n and a2ik 6≡ −1 mod n for all i 2 f1; : : : ; e−1g, where this range of values for i is empty if e = 1 (that is, if n ≡ 3 mod 4). Example 2.4. If n ≡ 3 mod 4 then e = 1 (and conversely). In this case k = (n − 1)=2, so a is a Miller{Rabin witness for n if a(n−1)=2 6≡ ±1 mod n, while a is a Miller{Rabin nonwitness for n if a(n−1)=2 ≡ ±1 mod n. Miller{Rabin witnesses and nonwitnesses can also be described using the list of powers k 2k 4k 2e−1k 2ik e−1 (2.2) (a ; a ; a ; : : : ; a ) = (fa g)i=0 with all terms considered modulo n. We call this the Miller{Rabin sequence for n that is generated by a. For example, to write a Miller{Rabin sequence for n = 57 write 57 − 1 = 23 · 7. Since e = 3 and k = 7, the Miller{Rabin sequence for 57 that is generated by a is (a7; a14; a28). Each term in a Miller{Rabin sequence is the square of the previous term, so if 1 occurs in the sequence then all later terms are 1. If −1 occurs in the sequence then all later terms are also 1. Thus −1 can occur at most once in this sequence. If 1 ≤ a ≤ n − 1 then a is a Miller{Rabin nonwitness for n if and only if (2.2) looks like (1;::: ) mod n or (:::; −1;::: ) mod n and a is a Miller{Rabin witness for n if and only if (2.2) is anything else: the first term is not 1 (equivalently, the terms in the Miller{Rabin sequence are not all 1) and there is no −1 anywhere in (2.2). So 1 and n − 1 are always Miller-Rabin nonwitnesses for n. Example 2.5. Let n = 9. Since n − 1 = 8 = 23, e = 3 and k = 1. The Miller{Rabin sequence for 9 generated by a is (a; a2; a4) mod 9. In the table below we list this sequence for a = 1; 2;:::; 8. The Miller{Rabin witnesses for 9 are 2, 3, 4, 5, 6, and 7. a mod 9 1 2 3 4 5 6 7 8 a2 mod 9 1 4 0 7 7 0 4 1 a4 mod 9 1 7 0 4 4 0 7 1 Example 2.6. Let n = 29341. Since n − 1 = 22 · 7335, the Miller{Rabin sequence for n generated by a is (ak; a2k) mod n where k = 7335. When a = 2, the Miller{Rabin sequence is (26424; 29340). The last term is −1 mod n, so −1 appears and therefore 2 is not a Miller{ Rabin witness for n. When a = 3 the Miller{Rabin sequence is (22569; 1). The first term is not 1 and no term is −1, so 3 is a Miller{Rabin witness for n and thus n is composite. THE MILLER{RABIN TEST 3 Example 2.7. Let n = 30121. Since n − 1 = 23 · 3765, the Miller{Rabin sequence for n generated by a is (ak; a2k; a4k) mod n where k = 3765. When a = 2, this sequence is (15036,73657, 39898,1,1). The first term is not 1 and no term is −1 mod n, so 2 is a Miller{Rabin witness for n. Example 2.8. Let n = 75361. Since n − 1 = 25 · 2355, the Miller{Rabin sequence for n generated by a is (ak; a2k; a4k; a8k; a16k) mod n where k = 2355. When a = 2, this sequence is (330, 18537,1). The first term is not 1 and there is no −1 mod n, so 2 is a Miller{Rabin witness for n. The first odd composite number not having 2 or 3 as an Euler witness is 1729, while the first odd composite number not having 2 or 3 as a Miller{Rabin witness is n = 1373653. Since n−1 = 22 ·343413, a Miller{Rabin sequence for n is (ak; a2k) mod n where k = 343413. The Miller{Rabin sequence generated by 2 is (890592; 137652), with the last term being −1 mod n, and the Miller{Rabin sequence generated by 3 is (1; 1). The number 5 is a Miller{Rabin witness for n: it generates the Miller{Rabin sequence (1199564, 73782). An exhaustive computer search has shown that every odd positive composite number less than 1010 has 2, 3, 5, or 7 as a Miller{Rabin witness except for 3215031751, and 11 is a Miller{ Rabin witness for that number. There is a more intuitive way to think about Miller{Rabin witnesses. If n is an odd prime the congruence an−1 ≡ 1 mod n from Fermat's little theorem can be rewritten as (ak)2e ≡ 1 mod n, so ak mod n has order equal to a power of 2 that is at most 2e. Therefore if ak 6≡ 1 mod n the order of ak mod n is 2j for some j 2 f1; : : : ; eg, so x := a2j−1k mod n has x2 ≡ 1 mod n and x 6≡ 1 mod n. The only square roots of unity modulo an odd prime n are ±1 mod n, so if ak 6≡ 1 mod n and none of the numbers a; a2; a4; : : : ; a2e−1k is −1 mod n then we have a contradiction: n can't be prime. We have rediscovered the definition of a Miller{Rabin nonwitness, and it shows us that the idea behind Miller{Rabin witnesses is to find an unexpected square root of 1 mod n. This is not always what we actually find, since the premise an−1 ≡ 1 mod n for prime n might not actually be true for composite n. In Example 2.5, for instance, no Miller{Rabin witness for 9 is a square root of 1 mod 9.
Recommended publications
  • Primality Testing and Integer Factorisation
    Primality Testing and Integer Factorisation Richard P. Brent, FAA Computer Sciences Laboratory Australian National University Canberra, ACT 2601 Abstract The problem of finding the prime factors of large composite numbers has always been of mathematical interest. With the advent of public key cryptosystems it is also of practical importance, because the security of some of these cryptosystems, such as the Rivest-Shamir-Adelman (RSA) system, depends on the difficulty of factoring the public keys. In recent years the best known integer factorisation algorithms have improved greatly, to the point where it is now easy to factor a 60-decimal digit number, and possible to factor numbers larger than 120 decimal digits, given the availability of enough computing power. We describe several recent algorithms for primality testing and factorisation, give examples of their use and outline some applications. 1. Introduction It has been known since Euclid’s time (though first clearly stated and proved by Gauss in 1801) that any natural number N has a unique prime power decomposition α1 α2 αk N = p1 p2 ··· pk (1.1) αj (p1 < p2 < ··· < pk rational primes, αj > 0). The prime powers pj are called αj components of N, and we write pj kN. To compute the prime power decomposition we need – 1. An algorithm to test if an integer N is prime. 2. An algorithm to find a nontrivial factor f of a composite integer N. Given these there is a simple recursive algorithm to compute (1.1): if N is prime then stop, otherwise 1. find a nontrivial factor f of N; 2.
    [Show full text]
  • An Analysis of Primality Testing and Its Use in Cryptographic Applications
    An Analysis of Primality Testing and Its Use in Cryptographic Applications Jake Massimo Thesis submitted to the University of London for the degree of Doctor of Philosophy Information Security Group Department of Information Security Royal Holloway, University of London 2020 Declaration These doctoral studies were conducted under the supervision of Prof. Kenneth G. Paterson. The work presented in this thesis is the result of original research carried out by myself, in collaboration with others, whilst enrolled in the Department of Mathe- matics as a candidate for the degree of Doctor of Philosophy. This work has not been submitted for any other degree or award in any other university or educational establishment. Jake Massimo April, 2020 2 Abstract Due to their fundamental utility within cryptography, prime numbers must be easy to both recognise and generate. For this, we depend upon primality testing. Both used as a tool to validate prime parameters, or as part of the algorithm used to generate random prime numbers, primality tests are found near universally within a cryptographer's tool-kit. In this thesis, we study in depth primality tests and their use in cryptographic applications. We first provide a systematic analysis of the implementation landscape of primality testing within cryptographic libraries and mathematical software. We then demon- strate how these tests perform under adversarial conditions, where the numbers being tested are not generated randomly, but instead by a possibly malicious party. We show that many of the libraries studied provide primality tests that are not pre- pared for testing on adversarial input, and therefore can declare composite numbers as being prime with a high probability.
    [Show full text]
  • Primes and Primality Testing
    Primes and Primality Testing A Technological/Historical Perspective Jennifer Ellis Department of Mathematics and Computer Science What is a prime number? A number p greater than one is prime if and only if the only divisors of p are 1 and p. Examples: 2, 3, 5, and 7 A few larger examples: 71887 524287 65537 2127 1 Primality Testing: Origins Eratosthenes: Developed “sieve” method 276-194 B.C. Nicknamed Beta – “second place” in many different academic disciplines Also made contributions to www-history.mcs.st- geometry, approximation of andrews.ac.uk/PictDisplay/Eratosthenes.html the Earth’s circumference Sieve of Eratosthenes 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 Sieve of Eratosthenes We only need to “sieve” the multiples of numbers less than 10. Why? (10)(10)=100 (p)(q)<=100 Consider pq where p>10. Then for pq <=100, q must be less than 10. By sieving all the multiples of numbers less than 10 (here, multiples of q), we have removed all composite numbers less than 100.
    [Show full text]
  • Primality Testing for Beginners
    STUDENT MATHEMATICAL LIBRARY Volume 70 Primality Testing for Beginners Lasse Rempe-Gillen Rebecca Waldecker http://dx.doi.org/10.1090/stml/070 Primality Testing for Beginners STUDENT MATHEMATICAL LIBRARY Volume 70 Primality Testing for Beginners Lasse Rempe-Gillen Rebecca Waldecker American Mathematical Society Providence, Rhode Island Editorial Board Satyan L. Devadoss John Stillwell Gerald B. Folland (Chair) Serge Tabachnikov The cover illustration is a variant of the Sieve of Eratosthenes (Sec- tion 1.5), showing the integers from 1 to 2704 colored by the number of their prime factors, including repeats. The illustration was created us- ing MATLAB. The back cover shows a phase plot of the Riemann zeta function (see Appendix A), which appears courtesy of Elias Wegert (www.visual.wegert.com). 2010 Mathematics Subject Classification. Primary 11-01, 11-02, 11Axx, 11Y11, 11Y16. For additional information and updates on this book, visit www.ams.org/bookpages/stml-70 Library of Congress Cataloging-in-Publication Data Rempe-Gillen, Lasse, 1978– author. [Primzahltests f¨ur Einsteiger. English] Primality testing for beginners / Lasse Rempe-Gillen, Rebecca Waldecker. pages cm. — (Student mathematical library ; volume 70) Translation of: Primzahltests f¨ur Einsteiger : Zahlentheorie - Algorithmik - Kryptographie. Includes bibliographical references and index. ISBN 978-0-8218-9883-3 (alk. paper) 1. Number theory. I. Waldecker, Rebecca, 1979– author. II. Title. QA241.R45813 2014 512.72—dc23 2013032423 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy a chapter for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given.
    [Show full text]
  • Miller–Rabin Test
    Lecture 31: Miller–Rabin Test Miller–Rabin Test Recall In the previous lecture we considered an efficient randomized algorithm to generate prime numbers that need n-bits in their binary representation This algorithm sampled a random element in the range f2n−1; 2n−1 + 1;:::; 2n − 1g and test whether it is a prime number or not By the Prime Number Theorem, we are extremely likely to hit a prime number So, all that remains is an algorithm to test whether the random sample we have chosen is a prime number or not Miller–Rabin Test Primality Testing Given an n-bit number N as input, we have to ascertain whether N is a prime number or not in time polynomial in n Only in 2002, Agrawal–Kayal–Saxena constructed a deterministic polynomial time algorithm for primality testing. That is, the algorithm will always run in time polynomial in n. For any input N (that has n-bits in its binary representation), if N is a prime number, the AKS primality testing algorithm will return 1; otherwise (if, the number N is a composite number), the AKS primality testing algorithm will return 0. In practice, this algorithm is not used for primality testing because this turns out to be too slow. In practice, we use a randomized algorithm, namely, the Miller–Rabin Test, that successfully distinguishes primes from composites with very high probability. In this lecture, we will study a basic version of this Miller–Rabin primality test. Miller–Rabin Test Assurance of Miller–Rabin Test Miller–Rabin outputs 1 to indicate that it has classified the input N as a prime.
    [Show full text]
  • 15-251: Great Theoretical Ideas in Computer Science Recitation 14 Solutions PRIMES ∈ NP
    15-251: Great Theoretical Ideas In Computer Science Recitation 14 Solutions PRIMES 2 NP The set of PRIMES of all primes is in co-NP, because if n is composite and k j n, we can verify this in polynomial time. In fact, the AKS primality test means that PRIMES is in P. We'll just prove PRIMES 2 NP. ∗ (a) We know n is prime iff φ(n) = n−1. Additionally, if n is prime then Zn is cyclic with order n−1. ∗ Given a generator a of Zn and a (guaranteed correct) prime factorization of n − 1, can we verify n is prime? If a has order n − 1, then we are done. So we must verify a has order dividing n − 1: an−1 ≡ 1 mod n and doesn't have smaller order, i.e. for all prime p j n − 1: a(n−1)=p 6≡ 1 mod n a is smaller than n, and there are at most log(n) factors each smaller than n, so our certificate is polynomial in n's representation. Similarly, each arithmetic computation is polynomial in log(n), and there are O(log(n)) of them. 1 (b) What else needs to be verified to use this as a primality certificate? Do we need to add more information? We also need to verify that our factorization (p1; α1); (p2; α2);:::; (pk; αk) of n − 1 is correct, two conditions: α1 α2 αk 1. p1 p2 : : : pk = n − 1 can be verified by multiplying and comparing, polynomial in log(n). 2.
    [Show full text]
  • Pseudoprimes and Classical Primality Tests
    Pseudoprimes and Classical Primality Tests Sebastian Pancratz 2 March 2009 1 Introduction This brief introduction concerns various notions of pseudoprimes and their relation to classical composite tests, that is, algorithms which can prove numbers composite, but might not identify all composites as such and hence cannot prove numbers prime. In the talk I hope to also present one of the classical algorithms to prove a number n prime. In contrast to modern algorithms, these are often either not polynomial-time algorithms, heavily depend on known factorisations of numbers such as n − 1 or are restricted to certain classes of candidates n. One of the simplest composite tests is based on Fermat's Little Theorem, stating that for any prime p and any integer a coprime to p we have ap−1 ≡ 1 (mod p). Hence we can prove an integer n > 1 composite by nding an integer b coprime to n with the property that bn−1 6≡ 1 (mod n). Denition 1. Let n > 1 be an odd integer and b > 1 with (b, n) = 1. We call n a pseudo-prime to base b if bn−1 ≡ 1 (mod n), that is, if the pair n, b satises the conclusion of Fermat's Little Theorem. Denition 2. If n is a positive odd composite integer which is a pseudo-prime to all bases b, we call n a Carmichael number. Using this criterion, we can easily check that e.g. 561 = 3 × 11 × 17 is a Carmichael number. It has been shown that there are innitely many Carmichael numbers [1].
    [Show full text]
  • Primality Testing and Sub-Exponential Factorization
    Primality Testing and Sub-Exponential Factorization David Emerson Advisor: Howard Straubing Boston College Computer Science Senior Thesis May, 2009 Abstract This paper discusses the problems of primality testing and large number factorization. The first section is dedicated to a discussion of primality test- ing algorithms and their importance in real world applications. Over the course of the discussion the structure of the primality algorithms are devel- oped rigorously and demonstrated with examples. This section culminates in the presentation and proof of the modern deterministic polynomial-time Agrawal-Kayal-Saxena algorithm for deciding whether a given n is prime. The second section is dedicated to the process of factorization of large com- posite numbers. While primality and factorization are mathematically tied in principle they are very di⇥erent computationally. This fact is explored and current high powered factorization methods and the mathematical structures on which they are built are examined. 1 Introduction Factorization and primality testing are important concepts in mathematics. From a purely academic motivation it is an intriguing question to ask how we are to determine whether a number is prime or not. The next logical question to ask is, if the number is composite, can we calculate its factors. The two questions are invariably related. If we can factor a number into its pieces then it is obviously not prime, if we can’t then we know that it is prime. The definition of primality is very much derived from factorability. As we progress through the known and developed primality tests and factorization algorithms it will begin to become clear that while primality and factorization are intertwined they occupy two very di⇥erent levels of computational di⇧culty.
    [Show full text]
  • Primality Testing
    Syracuse University SURFACE Electrical Engineering and Computer Science - Technical Reports College of Engineering and Computer Science 6-1992 Primality Testing Per Brinch Hansen Syracuse University, School of Computer and Information Science, [email protected] Follow this and additional works at: https://surface.syr.edu/eecs_techreports Part of the Computer Sciences Commons Recommended Citation Hansen, Per Brinch, "Primality Testing" (1992). Electrical Engineering and Computer Science - Technical Reports. 169. https://surface.syr.edu/eecs_techreports/169 This Report is brought to you for free and open access by the College of Engineering and Computer Science at SURFACE. It has been accepted for inclusion in Electrical Engineering and Computer Science - Technical Reports by an authorized administrator of SURFACE. For more information, please contact [email protected]. SU-CIS-92-13 Primality Testing Per Brinch Hansen June 1992 School of Computer and Information Science Syracuse University Suite 4-116, Center for Science and Technology Syracuse, NY 13244-4100 Primality Testing1 PER BRINCH HANSEN Syracuse University, Syracuse, New York 13244 June 1992 This tutorial describes the Miller-Rabin method for testing the primality of large integers. The method is illustrated by a Pascal algorithm. The performance of the algorithm was measured on a Computing Surface. Categories and Subject Descriptors: G.3 [Probability and Statistics: [Proba­ bilistic algorithms (Monte Carlo) General Terms: Algorithms Additional Key Words and Phrases: Primality testing CONTENTS INTRODUCTION 1. FERMAT'S THEOREM 2. THE FERMAT TEST 3. QUADRATIC REMAINDERS 4. THE MILLER-RABIN TEST 5. A PROBABILISTIC ALGORITHM 6. COMPLEXITY 7. EXPERIMENTS 8. SUMMARY ACKNOWLEDGEMENTS REFERENCES 1Copyright@1992 Per Brinch Hansen Per Brinch Hansen: Primality Testing 2 INTRODUCTION This tutorial describes a probabilistic method for testing the primality of large inte­ gers.
    [Show full text]
  • Prime Numbers and Discrete Logarithms
    Lecture 11: Prime Numbers And Discrete Logarithms Lecture Notes on “Computer and Network Security” by Avi Kak ([email protected]) February 25, 2021 12:20 Noon ©2021 Avinash Kak, Purdue University Goals: • Primality Testing • Fermat’s Little Theorem • The Totient of a Number • The Miller-Rabin Probabilistic Algorithm for Testing for Primality • Python and Perl Implementations for the Miller-Rabin Primal- ity Test • The AKS Deterministic Algorithm for Testing for Primality • Chinese Remainder Theorem for Modular Arithmetic with Large Com- posite Moduli • Discrete Logarithms CONTENTS Section Title Page 11.1 Prime Numbers 3 11.2 Fermat’s Little Theorem 5 11.3 Euler’s Totient Function 11 11.4 Euler’s Theorem 14 11.5 Miller-Rabin Algorithm for Primality Testing 17 11.5.1 Miller-Rabin Algorithm is Based on an Intuitive Decomposition of 19 an Even Number into Odd and Even Parts 11.5.2 Miller-Rabin Algorithm Uses the Fact that x2 =1 Has No 20 Non-Trivial Roots in Zp 11.5.3 Miller-Rabin Algorithm: Two Special Conditions That Must Be 24 Satisfied By a Prime 11.5.4 Consequences of the Success and Failure of One or Both Conditions 28 11.5.5 Python and Perl Implementations of the Miller-Rabin 30 Algorithm 11.5.6 Miller-Rabin Algorithm: Liars and Witnesses 39 11.5.7 Computational Complexity of the Miller-Rabin Algorithm 41 11.6 The Agrawal-Kayal-Saxena (AKS) Algorithm 44 for Primality Testing 11.6.1 Generalization of Fermat’s Little Theorem to Polynomial Rings 46 Over Finite Fields 11.6.2 The AKS Algorithm: The Computational Steps 51 11.6.3 Computational Complexity of the AKS Algorithm 53 11.7 The Chinese Remainder Theorem 54 11.7.1 A Demonstration of the Usefulness of CRT 58 11.8 Discrete Logarithms 61 11.9 Homework Problems 65 Computer and Network Security by Avi Kak Lecture 11 Back to TOC 11.1 PRIME NUMBERS • Prime numbers are extremely important to computer security.
    [Show full text]
  • MATH 453 Primality Testing We Have Seen a Few Primality Tests in Previous Chapters: Sieve of Eratosthenes (Trivial Divi- Sion), Wilson’S Test, and Fermat’S Test
    MATH 453 Primality Testing We have seen a few primality tests in previous chapters: Sieve of Eratosthenes (Trivial divi- sion), Wilson's Test, and Fermat's Test. • Sieve of Eratosthenes (or Trivial division) Let n 2 N with n > 1. p If n is not divisible by any integer d p≤ n, then n is prime. If n is divisible by some integer d ≤ n, then n is composite. • Wilson Test Let n 2 N with n > 1. If (n − 1)! 6≡ −1 mod n, then n is composite. If (n − 1)! ≡ −1 mod n, then n is prime. • Fermat Test Let n 2 N and a 2 Z with n > 1 and (a; n) = 1: If an−1 6≡ 1 mod n; then n is composite. If an−1 ≡ 1 mod n; then the test is inconclusive. Remark 1. Using the same assumption as above, if n is composite and an−1 ≡ 1 mod n, then n is said to be a pseudoprime to base a. If n is a pseudoprime to base a for all a 2 Z such that (a; n) = 1, then n is called a Carmichael num- ber. It is known that there are infinitely Carmichael numbers (hence infinitely many pseudoprimes to any base a). However, pseudoprimes are generally very rare. For example, among the first 1010 positive integers, 14; 882 integers (≈ 0:00015%) are pseudoprimes (to base 2), compared with 455; 052; 511 primes (≈ 4:55%) In reality, thep primality tests listed above are computationally inefficient. For instance, it takes up to n divisions to determine whether n is prime using the Trivial Division method.
    [Show full text]
  • Primality Testing One of the Most Important Domains for Randomized Al
    CMPSCI611: Primality Testing Lecture 14 One of the most important domains for randomized al- gorithms is number theory, particularly with its appli- cations to cryptography. Today we’ll look at one of the several fast randomized algorithms for primality testing, but let’s first set some context. When we operate on very large integers, our normal mea- sure of the input size is the number of bits required to store the number: log n for an integer n. In practice we might store the integer in a list of machine words, as in the Java Bignum class, but since only a constant number of bits will fit in a machine word, O(log n) is still a good measure of the size of n. We can add, subtract, multiply, or divide large numbers in time linear or nearly linear in their size. We can run the Euclidean algorithm on two numbers and thus find whether they have a common factor. But other opera- tions that we take for granted on small integers appear to become much harder on large ones. 1 How do we test small numbers to see whether they are prime? Using the definition, we get the trial division√ method, where we try every number from 2 through√ n to see whether it divides n exactly. This takes O( n) time, which looks promising until you realize that this time is exponential in log n. Until recently, no determin- istic algorithm was known that ran in (log n)O(1) time. This theoretical challenge was met in 2004 by Agrawal, Kayal, and Saxena, who have an algorithm that does it in about O((log n)15/2) and probably somewhat faster, about O((log n)6).
    [Show full text]