13.8 Factoring and Primality Testing
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Fast Tabulation of Challenge Pseudoprimes Andrew Shallue and Jonathan Webster
THE OPEN BOOK SERIES 2 ANTS XIII Proceedings of the Thirteenth Algorithmic Number Theory Symposium Fast tabulation of challenge pseudoprimes Andrew Shallue and Jonathan Webster msp THE OPEN BOOK SERIES 2 (2019) Thirteenth Algorithmic Number Theory Symposium msp dx.doi.org/10.2140/obs.2019.2.411 Fast tabulation of challenge pseudoprimes Andrew Shallue and Jonathan Webster We provide a new algorithm for tabulating composite numbers which are pseudoprimes to both a Fermat test and a Lucas test. Our algorithm is optimized for parameter choices that minimize the occurrence of pseudoprimes, and for pseudoprimes with a fixed number of prime factors. Using this, we have confirmed that there are no PSW-challenge pseudoprimes with two or three prime factors up to 280. In the case where one is tabulating challenge pseudoprimes with a fixed number of prime factors, we prove our algorithm gives an unconditional asymptotic improvement over previous methods. 1. Introduction Pomerance, Selfridge, and Wagstaff famously offered $620 for a composite n that satisfies (1) 2n 1 1 .mod n/ so n is a base-2 Fermat pseudoprime, Á (2) .5 n/ 1 so n is not a square modulo 5, and j D (3) Fn 1 0 .mod n/ so n is a Fibonacci pseudoprime, C Á or to prove that no such n exists. We call composites that satisfy these conditions PSW-challenge pseudo- primes. In[PSW80] they credit R. Baillie with the discovery that combining a Fermat test with a Lucas test (with a certain specific parameter choice) makes for an especially effective primality test[BW80]. -
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. -
Computing Prime Divisors in an Interval
MATHEMATICS OF COMPUTATION Volume 84, Number 291, January 2015, Pages 339–354 S 0025-5718(2014)02840-8 Article electronically published on May 28, 2014 COMPUTING PRIME DIVISORS IN AN INTERVAL MINKYU KIM AND JUNG HEE CHEON Abstract. We address the problem of finding a nontrivial divisor of a com- posite integer when it has a prime divisor in an interval. We show that this problem can be solved in time of the square root of the interval length with a similar amount of storage, by presenting two algorithms; one is probabilistic and the other is its derandomized version. 1. Introduction Integer factorization is one of the most challenging problems in computational number theory. It has been studied for centuries, and it has been intensively in- vestigated after introducing the RSA cryptosystem [18]. The difficulty of integer factorization has been examined not only in pure factoring situations but also in various modified situations. One such approach is to find a nontrivial divisor of a composite integer when it has prime divisors of special form. These include Pol- lard’s p − 1 algorithm [15], Williams’ p + 1 algorithm [20], and others. On the other hand, owing to the importance and the usage of integer factorization in cryptog- raphy, one needs to examine this problem when some partial information about divisors is revealed. This side information might be exposed through the proce- dures of generating prime numbers or through some attacks against crypto devices or protocols. This paper deals with integer factorization given the approximation of divisors, and it is motivated by the above mentioned research. -
Fast Generation of RSA Keys Using Smooth Integers
1 Fast Generation of RSA Keys using Smooth Integers Vassil Dimitrov, Luigi Vigneri and Vidal Attias Abstract—Primality generation is the cornerstone of several essential cryptographic systems. The problem has been a subject of deep investigations, but there is still a substantial room for improvements. Typically, the algorithms used have two parts – trial divisions aimed at eliminating numbers with small prime factors and primality tests based on an easy-to-compute statement that is valid for primes and invalid for composites. In this paper, we will showcase a technique that will eliminate the first phase of the primality testing algorithms. The computational simulations show a reduction of the primality generation time by about 30% in the case of 1024-bit RSA key pairs. This can be particularly beneficial in the case of decentralized environments for shared RSA keys as the initial trial division part of the key generation algorithms can be avoided at no cost. This also significantly reduces the communication complexity. Another essential contribution of the paper is the introduction of a new one-way function that is computationally simpler than the existing ones used in public-key cryptography. This function can be used to create new random number generators, and it also could be potentially used for designing entirely new public-key encryption systems. Index Terms—Multiple-base Representations, Public-Key Cryptography, Primality Testing, Computational Number Theory, RSA ✦ 1 INTRODUCTION 1.1 Fast generation of prime numbers DDITIVE number theory is a fascinating area of The generation of prime numbers is a cornerstone of A mathematics. In it one can find problems with cryptographic systems such as the RSA cryptosystem. -
The Quadratic Sieve Factoring Algorithm
The Quadratic Sieve Factoring Algorithm Eric Landquist MATH 488: Cryptographic Algorithms December 14, 2001 1 1 Introduction Mathematicians have been attempting to find better and faster ways to fac- tor composite numbers since the beginning of time. Initially this involved dividing a number by larger and larger primes until you had the factoriza- tion. This trial division was not improved upon until Fermat applied the factorization of the difference of two squares: a2 b2 = (a b)(a + b). In his method, we begin with the number to be factored:− n. We− find the smallest square larger than n, and test to see if the difference is square. If so, then we can apply the trick of factoring the difference of two squares to find the factors of n. If the difference is not a perfect square, then we find the next largest square, and repeat the process. While Fermat's method is much faster than trial division, when it comes to the real world of factoring, for example factoring an RSA modulus several hundred digits long, the purely iterative method of Fermat is too slow. Sev- eral other methods have been presented, such as the Elliptic Curve Method discovered by H. Lenstra in 1987 and a pair of probabilistic methods by Pollard in the mid 70's, the p 1 method and the ρ method. The fastest algorithms, however, utilize the− same trick as Fermat, examples of which are the Continued Fraction Method, the Quadratic Sieve (and it variants), and the Number Field Sieve (and its variants). The exception to this is the El- liptic Curve Method, which runs almost as fast as the Quadratic Sieve. -
Integer Factorization and Computing Discrete Logarithms in Maple
Integer Factorization and Computing Discrete Logarithms in Maple Aaron Bradford∗, Michael Monagan∗, Colin Percival∗ [email protected], [email protected], [email protected] Department of Mathematics, Simon Fraser University, Burnaby, B.C., V5A 1S6, Canada. 1 Introduction As part of our MITACS research project at Simon Fraser University, we have investigated algorithms for integer factorization and computing discrete logarithms. We have implemented a quadratic sieve algorithm for integer factorization in Maple to replace Maple's implementation of the Morrison- Brillhart continued fraction algorithm which was done by Gaston Gonnet in the early 1980's. We have also implemented an indexed calculus algorithm for discrete logarithms in GF(q) to replace Maple's implementation of Shanks' baby-step giant-step algorithm, also done by Gaston Gonnet in the early 1980's. In this paper we describe the algorithms and our optimizations made to them. We give some details of our Maple implementations and present some initial timings. Since Maple is an interpreted language, see [7], there is room for improvement of both implementations by coding critical parts of the algorithms in C. For example, one of the bottle-necks of the indexed calculus algorithm is finding and integers which are B-smooth. Let B be a set of primes. A positive integer y is said to be B-smooth if its prime divisors are all in B. Typically B might be the first 200 primes and y might be a 50 bit integer. ∗This work was supported by the MITACS NCE of Canada. 1 2 Integer Factorization Starting from some very simple instructions | \make integer factorization faster in Maple" | we have implemented the Quadratic Sieve factoring al- gorithm in a combination of Maple and C (which is accessed via Maple's capabilities for external linking). -
Cryptology and Computational Number Theory (Boulder, Colorado, August 1989) 41 R
http://dx.doi.org/10.1090/psapm/042 Other Titles in This Series 50 Robert Calderbank, editor, Different aspects of coding theory (San Francisco, California, January 1995) 49 Robert L. Devaney, editor, Complex dynamical systems: The mathematics behind the Mandlebrot and Julia sets (Cincinnati, Ohio, January 1994) 48 Walter Gautschi, editor, Mathematics of Computation 1943-1993: A half century of computational mathematics (Vancouver, British Columbia, August 1993) 47 Ingrid Daubechies, editor, Different perspectives on wavelets (San Antonio, Texas, January 1993) 46 Stefan A. Burr, editor, The unreasonable effectiveness of number theory (Orono, Maine, August 1991) 45 De Witt L. Sumners, editor, New scientific applications of geometry and topology (Baltimore, Maryland, January 1992) 44 Bela Bollobas, editor, Probabilistic combinatorics and its applications (San Francisco, California, January 1991) 43 Richard K. Guy, editor, Combinatorial games (Columbus, Ohio, August 1990) 42 C. Pomerance, editor, Cryptology and computational number theory (Boulder, Colorado, August 1989) 41 R. W. Brockett, editor, Robotics (Louisville, Kentucky, January 1990) 40 Charles R. Johnson, editor, Matrix theory and applications (Phoenix, Arizona, January 1989) 39 Robert L. Devaney and Linda Keen, editors, Chaos and fractals: The mathematics behind the computer graphics (Providence, Rhode Island, August 1988) 38 Juris Hartmanis, editor, Computational complexity theory (Atlanta, Georgia, January 1988) 37 Henry J. Landau, editor, Moments in mathematics (San Antonio, Texas, January 1987) 36 Carl de Boor, editor, Approximation theory (New Orleans, Louisiana, January 1986) 35 Harry H. Panjer, editor, Actuarial mathematics (Laramie, Wyoming, August 1985) 34 Michael Anshel and William Gewirtz, editors, Mathematics of information processing (Louisville, Kentucky, January 1984) 33 H. Peyton Young, editor, Fair allocation (Anaheim, California, January 1985) 32 R. -
Integer Factorization with a Neuromorphic Sieve
Integer Factorization with a Neuromorphic Sieve John V. Monaco and Manuel M. Vindiola U.S. Army Research Laboratory Aberdeen Proving Ground, MD 21005 Email: [email protected], [email protected] Abstract—The bound to factor large integers is dominated by to represent n, the computational effort to factor n by trial the computational effort to discover numbers that are smooth, division grows exponentially. typically performed by sieving a polynomial sequence. On a Dixon’s factorization method attempts to construct a con- von Neumann architecture, sieving has log-log amortized time 2 2 complexity to check each value for smoothness. This work gruence of squares, x ≡ y mod n. If such a congruence presents a neuromorphic sieve that achieves a constant time is found, and x 6≡ ±y mod n, then gcd (x − y; n) must be check for smoothness by exploiting two characteristic properties a nontrivial factor of n. A class of subexponential factoring of neuromorphic architectures: constant time synaptic integration algorithms, including the quadratic sieve, build on Dixon’s and massively parallel computation. The approach is validated method by specifying how to construct the congruence of by modifying msieve, one of the fastest publicly available integer factorization implementations, to use the IBM Neurosynaptic squares through a linear combination of smooth numbers [5]. System (NS1e) as a coprocessor for the sieving stage. Given smoothness bound B, a number is B-smooth if it does not contain any prime factors greater than B. Additionally, let I. INTRODUCTION v = e1; e2; : : : ; eπ(B) be the exponents vector of a smooth number s, where s = Q pvi , p is the ith prime, and A number is said to be smooth if it is an integer composed 1≤i≤π(B) i i π (B) is the number of primes not greater than B. -
Cracking RSA with Various Factoring Algorithms Brian Holt
Cracking RSA with Various Factoring Algorithms Brian Holt 1 Abstract For centuries, factoring products of large prime numbers has been recognized as a computationally difficult task by mathematicians. The modern encryption scheme RSA (short for Rivest, Shamir, and Adleman) uses products of large primes for secure communication protocols. In this paper, we analyze and compare four factorization algorithms which use elementary number theory to assess the safety of various RSA moduli. 2 Introduction The origins of prime factorization can be traced back to around 300 B.C. when Euclid's theorem and the unique factorization theorem were proved in his work El- ements [3]. For nearly two millenia, the simple trial division algorithm was used to factor numbers into primes. More efficient means were studied by mathemeticians during the seventeenth and eighteenth centuries. In the 1640s, Pierre de Fermat devised what is known as Fermat's factorization method. Over one century later, Leonhard Euler improved upon Fermat's factorization method for numbers that take specific forms. Around this time, Adrien-Marie Legendre and Carl Gauss also con- tributed crucial ideas used in modern factorization schemes [3]. Prime factorization's potential for secure communication was not recognized until the twentieth century. However, economist William Jevons antipicated the "one-way" or ”difficult-to-reverse" property of products of large primes in 1874. This crucial con- cept is the basis of modern public key cryptography. Decrypting information without access to the private key must be computationally complex to prevent malicious at- tacks. In his book The Principles of Science: A Treatise on Logic and Scientific Method, Jevons challenged the reader to factor a ten digit semiprime (now called Jevon's Number)[4]. -
Tables of Fibonacci and Lucas Factorizations
MATHEMATICS OF COMPUTATION VOLUME 50, NUMBER 181 JANUARY 1988, PAGES 251-260 Tables of Fibonacci and Lucas Factorizations By John Brillhart, Peter L. Montgomery, and Robert D. Silverman Dedicated to Dov Jarden Abstract. We list the known prime factors of the Fibonacci numbers Fn for n < 999 and Lucas numbers Ln for n < 500. We discuss the various methods used to obtain these factorizations, and primality tests, and give some history of the subject. 1. Introduction. In the Supplements section at the end of this issue we give in two tables the known prime factors of the Fibonacci numbers Fn, 3 < n < 999, n odd, and the Lucas numbers Ln, 2 < n < 500. The sequences Fn and Ln are defined recursively by the formulas . ^n+2 = Fn+X + Fn, Fo = 0, Fi = 1, Ln+2 = Ln+i + Ln, in = 2, L\ = 1. The use of a different subscripting destroys the divisibility properties of these numbers. We also have the formulas an - 3a (1-2) Fn = --£-, Ln = an + ßn, a —ß where a = (1 + \/B)/2 and ß — (1 - v^5)/2. This paper is concerned with the multiplicative structure of Fn and Ln. It includes both theoretical and numerical results. 2. Multiplicative Structure of Fn and Ln. The identity (2.1) F2n = FnLn follows directly from (1.2). Although the Fibonacci and Lucas numbers are defined additively, this is one of many multiplicative identities relating these sequences. The identities in this paper are derived from the familiar polynomial factorization (2.2) xn-yn = ]\*d(x,y), n>\, d\n where $d(x,y) is the dth cyclotomic polynomial in homogeneous form. -
Primality Testing and Integer Factorization in Public-Key Cryptography Pdf, Epub, Ebook
PRIMALITY TESTING AND INTEGER FACTORIZATION IN PUBLIC-KEY CRYPTOGRAPHY PDF, EPUB, EBOOK Song Y. Yan | 371 pages | 29 Nov 2010 | Springer-Verlag New York Inc. | 9781441945860 | English | New York, NY, United States Primality Testing and Integer Factorization in Public-Key Cryptography PDF Book RA 39 math. Blog at WordPress. The two equations give. Book Description Condition: New. Stock Image. The square and multiply algorithm is equivalent to the Python one-liner pow x, k, p. One can use a crude version of the prime number theorem to get the upper bound on. Since has order greater than in , we see that the number of residue classes of the form is at least. Factoring integers with elliptic curves. HO 12 math. They matter because of an important class of cryptosystems, in a multibillion dollar industry. You have to search a smaller space until you brute force the key. View 2 excerpts, references background and methods. By Terence Tao. So to establish the proposition it suffices to show that all these products are distinct. View all copies of this ISBN edition:. Hello I think that binomial test is also suitable for factoring. View 9 excerpts, references background. Each shard is capable of processing transactions in parallel, yielding a high throughput for the network. Sorry, your blog cannot share posts by email. Updates on my research and expository papers, discussion of open problems, and other maths-related topics. Note that can be computed in time for any fixed by expressing in binary, and repeatedly squaring. I implemented those steps in the Python code below. -
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.