Investigation Study of Feasible Prime Number Testing Algorithms
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Lecture 9: Arithmetics II 1 Greatest Common Divisor
DD2458, Problem Solving and Programming Under Pressure Lecture 9: Arithmetics II Date: 2008-11-10 Scribe(s): Marcus Forsell Stahre and David Schlyter Lecturer: Douglas Wikström This lecture is a continuation of the previous one, and covers modular arithmetic and topics from a branch of number theory known as elementary number theory. Also, some abstract algebra will be discussed. 1 Greatest Common Divisor Definition 1.1 If an integer d divides another integer n with no remainder, d is said to be a divisor of n. That is, there exists an integer a such that a · d = n. The notation for this is d | n. Definition 1.2 A common divisor of two non-zero integers m and n is a positive integer d, such that d | m and d | n. Definition 1.3 The Greatest Common Divisor (GCD) of two positive integers m and n is a common divisor d such that every other common divisor d0 | d. The notation for this is GCD(m, n) = d. That is, the GCD of two numbers is the greatest number that is a divisor of both of them. To get an intuition of what GCD is, let’s have a look at this example. Example Calculate GCD(9, 6). Say we have 9 black and 6 white blocks. We want to put the blocks in boxes, but every box has to be the same size, and can only hold blocks of the same color. Also, all the boxes must be full and as large as possible . Let’s for example say we choose a box of size 2: As we can see, the last box of black bricks is not full. -
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. -
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. -
Enclave Security and Address-Based Side Channels
Graz University of Technology Faculty of Computer Science Institute of Applied Information Processing and Communications IAIK Enclave Security and Address-based Side Channels Assessors: A PhD Thesis Presented to the Prof. Stefan Mangard Faculty of Computer Science in Prof. Thomas Eisenbarth Fulfillment of the Requirements for the PhD Degree by June 2020 Samuel Weiser Samuel Weiser Enclave Security and Address-based Side Channels DOCTORAL THESIS to achieve the university degree of Doctor of Technical Sciences; Dr. techn. submitted to Graz University of Technology Assessors Prof. Stefan Mangard Institute of Applied Information Processing and Communications Graz University of Technology Prof. Thomas Eisenbarth Institute for IT Security Universit¨atzu L¨ubeck Graz, June 2020 SSS AFFIDAVIT I declare that I have authored this thesis independently, that I have not used other than the declared sources/resources, and that I have explicitly indicated all material which has been quoted either literally or by content from the sources used. The text document uploaded to TUGRAZonline is identical to the present doctoral thesis. Date, Signature SSS Prologue Everyone has the right to life, liberty and security of person. Universal Declaration of Human Rights, Article 3 Our life turned digital, and so did we. Not long ago, the globalized commu- nication that we enjoy today on an everyday basis was the privilege of a few. Nowadays, artificial intelligence in the cloud, smartified handhelds, low-power Internet-of-Things gadgets, and self-maneuvering objects in the physical world are promising us unthinkable freedom in shaping our personal lives as well as society as a whole. Sadly, our collective excitement about the \new", the \better", the \more", the \instant", has overruled our sense of security and privacy. -
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). -
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]. -
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. -
Independence of the Miller-Rabin and Lucas Probable Prime Tests
Independence of the Miller-Rabin and Lucas Probable Prime Tests Alec Leng Mentor: David Corwin March 30, 2017 1 Abstract In the modern age, public-key cryptography has become a vital component for se- cure online communication. To implement these cryptosystems, rapid primality test- ing is necessary in order to generate keys. In particular, probabilistic tests are used for their speed, despite the potential for pseudoprimes. So, we examine the commonly used Miller-Rabin and Lucas tests, showing that numbers with many nonwitnesses are usually Carmichael or Lucas-Carmichael numbers in a specific form. We then use these categorizations, through a generalization of Korselt’s criterion, to prove that there are no numbers with many nonwitnesses for both tests, affirming the two tests’ relative independence. As Carmichael and Lucas-Carmichael numbers are in general more difficult for the two tests to deal with, we next search for numbers which are both Carmichael and Lucas-Carmichael numbers, experimentally finding none less than 1016. We thus conjecture that there are no such composites and, using multi- variate calculus with symmetric polynomials, begin developing techniques to prove this. 2 1 Introduction In the current information age, cryptographic systems to protect data have become a funda- mental necessity. With the quantity of data distributed over the internet, the importance of encryption for protecting individual privacy has greatly risen. Indeed, according to [EMC16], cryptography is allows for authentication and protection in online commerce, even when working with vital financial information (e.g. in online banking and shopping). Now that more and more transactions are done through the internet, rather than in person, the importance of secure encryption schemes is only growing. -
Implementing and Comparing Integer Factorization Algorithms
Implementing and Comparing Integer Factorization Algorithms Jacqueline Speiser jspeiser p Abstract by choosing B = exp( logN loglogN)) and let the factor base be the set of all primes smaller than B. Next, Integer factorization is an important problem in modern search for positive integers x such that x2 mod N is B- cryptography as it is the basis of RSA encryption. I have smooth, meaning that all the factors of x2 are in the factor implemented two integer factorization algorithms: Pol- 2 e1 e2 ek base. For all B-smooth numbers xi = p p ::: p , lard’s rho algorithm and Dixon’s factorization method. 2 record (xi ;~ei). After we have enough of these relations, While the results are not revolutionary, they illustrate we can solve a system of linear equations to find some the software design difficulties inherent to integer fac- subset of the relations such that ∑~ei =~0 mod 2. (See the torization. The code for this project is available at Implementation section for details on how this is done.) https://github.com/jspeiser/factoring. Note that if k is the size of our factor base, then we only need k + 1 relations to guarantee that such a solution 1 Introduction exists. We have now found a congruence of squares, 2 2 2 ∑i ei1 ∑i eik a = xi and b = p1 ::: pk . This implies that The integer factorization problem is defined as follows: (a + b)(a − b) = 0 mod N, which means that there is a given a composite number N, find two integers x and y 50% chance that gcd(a−b;N) factorspN.