The RSA Algorithm Clifton Paul Robinson
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The Quadratic Sieve - Introduction to Theory with Regard to Implementation Issues
The Quadratic Sieve - introduction to theory with regard to implementation issues RNDr. Marian Kechlibar, Ph.D. April 15, 2005 Contents I The Quadratic Sieve 3 1 Introduction 4 1.1 The Quadratic Sieve - short description . 5 1.1.1 Polynomials and relations . 5 1.1.2 Smooth and partial relations . 7 1.1.3 The Double Large Prime Variation . 8 1.1.4 Problems to solve . 10 2 Quadratic Sieve Implementation 12 2.1 The Factor Base . 12 2.2 The sieving process . 15 2.2.1 Interval sieving and solution of polynomials . 16 2.2.2 Practical implementation . 16 2.3 Generation of polynomials . 17 2.3.1 Desirable properties of polynomials . 17 2.3.2 Assessment of magnitude of coecients . 18 2.3.3 MPQS - The Silverman Method . 20 2.3.4 SIQS principle . 21 2.3.5 Desirable properties of b . 22 2.3.6 SIQS - Generation of the Bi's . 23 2.3.7 Generation of b with Gray code formulas . 24 2.3.8 SIQS - General remarks on a determination . 26 2.3.9 SIQS - The bit method for a coecient . 27 2.3.10 SIQS - The Carrier-Wagsta method for a coecient . 28 2.4 Combination of the relations, partial relations and linear algebra 30 2.5 Linear algebra step . 31 2.6 The Singleton Gap . 32 1 3 Experimental Results 36 3.1 Sieving speed - dependence on FB size . 36 3.2 Sieving speed - dependence on usage of 1-partials . 38 3.3 Singletons - dependence on log(N) and FB size . 39 3.4 Properties of the sieving matrices . -
Arxiv:1412.5226V1 [Math.NT] 16 Dec 2014 Hoe 11
q-PSEUDOPRIMALITY: A NATURAL GENERALIZATION OF STRONG PSEUDOPRIMALITY JOHN H. CASTILLO, GILBERTO GARC´IA-PULGAR´IN, AND JUAN MIGUEL VELASQUEZ-SOTO´ Abstract. In this work we present a natural generalization of strong pseudoprime to base b, which we have called q-pseudoprime to base b. It allows us to present another way to define a Midy’s number to base b (overpseudoprime to base b). Besides, we count the bases b such that N is a q-probable prime base b and those ones such that N is a Midy’s number to base b. Furthemore, we prove that there is not a concept analogous to Carmichael numbers to q-probable prime to base b as with the concept of strong pseudoprimes to base b. 1. Introduction Recently, Grau et al. [7] gave a generalization of Pocklignton’s Theorem (also known as Proth’s Theorem) and Miller-Rabin primality test, it takes as reference some works of Berrizbeitia, [1, 2], where it is presented an extension to the concept of strong pseudoprime, called ω-primes. As Grau et al. said it is right, but its application is not too good because it is needed m-th primitive roots of unity, see [7, 12]. In [7], it is defined when an integer N is a p-strong probable prime base a, for p a prime divisor of N −1 and gcd(a, N) = 1. In a reading of that paper, we discovered that if a number N is a p-strong probable prime to base 2 for each p prime divisor of N − 1, it is actually a Midy’s number or a overpseu- doprime number to base 2. -
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. -
Sieve Algorithms for the Discrete Logarithm in Medium Characteristic Finite Fields Laurent Grémy
Sieve algorithms for the discrete logarithm in medium characteristic finite fields Laurent Grémy To cite this version: Laurent Grémy. Sieve algorithms for the discrete logarithm in medium characteristic finite fields. Cryptography and Security [cs.CR]. Université de Lorraine, 2017. English. NNT : 2017LORR0141. tel-01647623 HAL Id: tel-01647623 https://tel.archives-ouvertes.fr/tel-01647623 Submitted on 24 Nov 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. AVERTISSEMENT Ce document est le fruit d'un long travail approuvé par le jury de soutenance et mis à disposition de l'ensemble de la communauté universitaire élargie. Il est soumis à la propriété intellectuelle de l'auteur. Ceci implique une obligation de citation et de référencement lors de l’utilisation de ce document. D'autre part, toute contrefaçon, plagiat, reproduction illicite encourt une poursuite pénale. Contact : [email protected] LIENS Code de la Propriété Intellectuelle. articles L 122. 4 Code de la Propriété Intellectuelle. articles L 335.2- L 335.10 http://www.cfcopies.com/V2/leg/leg_droi.php -
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). -
Generating Provable Primes Efficiently on Embedded Devices
Generating Provable Primes Efficiently on Embedded Devices Christophe Clavier1, Benoit Feix1;2, Lo¨ıc Thierry2;?, and Pascal Paillier3 1 XLIM, University of Limoges, [email protected] 2 INSIDE Secure [email protected],[email protected] 3 CryptoExperts [email protected] Abstract. This paper introduces new techniques to generate provable prime numbers efficiently on embedded devices such as smartcards, based on variants of Pocklington's and the Brillhart-Lehmer-Selfridge-Tuckerman- Wagstaff theorems. We introduce two new generators that, combined with cryptoprocessor-specific optimizations, open the way to efficient and tamper-resistant on-board generation of provable primes. We also report practical results from our implementations. Both our theoretical and ex- perimental results show that constructive methods can generate provable primes essentially as efficiently as state-of-the-art generators for probable primes based on Fermat and Miller-Rabin pseudo-tests. We evaluate the output entropy of our two generators and provide techniques to ensure a high level of resistance against physical attacks. This paper intends to provide practitioners with the first practical solutions for fast and secure generation of provable primes in embedded security devices. Keywords: Prime Numbers, Pocklington's theorem, Public Key Cryp- tography, Embedded Software, Modular Exponentiation, Cryptographic Accelerators, Primality Proving. 1 Introduction Large prime numbers are a basic ingredient of keys in several standardized primi- tives such as RSA [21], Digital Signature Algorithm (DSA) [12] or Diffie-Hellman key exchange (DH) [10]. This paper precisely addresses the generation of prov- able prime numbers in embedded, crypto-enabled devices. When it comes to RSA key generation, two approaches coexist: key pairs may be generated off-board (i.e. -
Factoring and Discrete Log
Factoring and Discrete Log Nadia Heninger University of Pennsylvania June 1, 2015 Textbook RSA [Rivest Shamir Adleman 1977] Public Key Private Key N = pq modulus p; q primes e encryption exponent d decryption exponent (d = e−1 mod (p − 1)(q − 1)) Encryption public key = (N; e) ciphertext = messagee mod N message = ciphertextd mod N Textbook RSA [Rivest Shamir Adleman 1977] Public Key Private Key N = pq modulus p; q primes e encryption exponent d decryption exponent (d = e−1 mod (p − 1)(q − 1)) Signing public key = (N; e) signature = messaged mod N message = signaturee mod N Computational problems Factoring Problem: Given N, compute its prime factors. I Computationally equivalent to computing private key d. I Factoring is in NP and coNP ! not NP-complete (unless P=NP or similar). Computational problems eth roots mod N Problem: Given N, e, and c, compute x such that xe ≡ c mod N. I Equivalent to decrypting an RSA-encrypted ciphertext. I Equivalent to selective forgery of RSA signatures. I Conflicting results about whether it reduces to factoring: I \Breaking RSA may not be equivalent to factoring" [Boneh Venkatesan 1998] \an algebraic reduction from factoring to breaking low-exponent RSA can be converted into an efficient factoring algorithm" I \Breaking RSA generically is equivalent to factoring" [Aggarwal Maurer 2009] \a generic ring algorithm for breaking RSA in ZN can be converted into an algorithm for factoring" I \RSA assumption": This problem is hard. A garden of attacks on textbook RSA Unpadded RSA encryption is homomorphic under multiplication. Let's have some fun! Attack: Malleability Given a ciphertext c = Enc(m) = me mod N, attacker can forge ciphertext Enc(ma) = cae mod N for any a. -
Factoring Integers with a Brain-Inspired Computer John V
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: REGULAR PAPERS 1 Factoring Integers with a Brain-Inspired Computer John V. Monaco and Manuel M. Vindiola Abstract—The bound to factor large integers is dominated • Constant-time synaptic integration: a single neuron in the by the computational effort to discover numbers that are B- brain may receive electrical potential inputs along synap- smooth, i.e., integers whose largest prime factor does not exceed tic connections from thousands of other neurons. The B. Smooth numbers are traditionally discovered by sieving a polynomial sequence, whereby the logarithmic sum of prime incoming potentials are continuously and instantaneously factors of each polynomial value is compared to a threshold. integrated to compute the neuron’s membrane potential. On a von Neumann architecture, this requires a large block of Like the brain, neuromorphic architectures aim to per- memory for the sieving interval and frequent memory updates, form synaptic integration in constant time, typically by resulting in O(ln ln B) amortized time complexity to check each leveraging physical properties of the underlying device. value for smoothness. This work presents a neuromorphic sieve that achieves a constant-time check for smoothness by reversing Unlike the traditional CPU-based sieve, the factor base is rep- the roles of space and time from the von Neumann architecture resented in space (as spiking neurons) and the sieving interval and exploiting two characteristic properties of brain-inspired in time (as successive time steps). Sieving is performed by computation: massive parallelism and constant time synaptic integration. The effects on sieving performance of two common a population of leaky integrate-and-fire (LIF) neurons whose neuromorphic architectural constraints are examined: limited dynamics are simple enough to be implemented on a range synaptic weight resolution, which forces the factor base to be of current and future architectures. -
The Probability That a Random Probable Prime Is Composite*
MATHEMATICS OF COMPUTATION VOLUME 53, NUMBER 188 OCTOBER 1989, PAGES 721-741 The Probability that a Random Probable Prime is Composite* By Su Hee Kim and Carl Pomerance** Abstract. Consider a procedure which (1) chooses a random odd number n < x, (2) chooses a random number 6, 1 < b < n — 1, and (3) accepts n if bn~l = 1 (mod n). Let P(x) denote the probability that this procedure accepts a composite number. It is known from work of Erdös and the second author that P(x) —>0 as x —►oo. In this paper, explicit inequalities are established for P(x)\ For example, it is shown that P(10100) < 2.77 x 10~8 and that P(x) < (logx)"197 for x > 101()5. Introduction. Suppose one wants to produce a random prime p < x, drawn with the uniform distribution. One possible solution is to choose a random number n, 1 < n < x, and apply a test to n that can tell if it is prime or composite. This procedure is repeated independently until a prime is found. By the prime number theorem, the expected number of trials until a prime is drawn is about log x. If one wishes to choose an odd prime, the trials n may be restricted to odd numbers. The expected number of trials is then about ¿ log x. There are many algorithms which can be used to decide if n is prime or com- posite. However, using the Fermât congruence is a very cheap test that is usually recommended as a preliminary procedure before a more time-consuming test is at- tempted. -
Quadratic Frobenius Probable Prime Tests Costing Two Selfridges
Quadratic Frobenius probable prime tests costing two selfridges Paul Underwood June 6, 2017 Abstract By an elementary observation about the computation of the difference of squares for large in- tegers, deterministic quadratic Frobenius probable prime tests are given with running times of approximately 2 selfridges. 1 Introduction Much has been written about Fermat probable prime (PRP) tests [1, 2, 3], Lucas PRP tests [4, 5], Frobenius PRP tests [6, 7, 8, 9, 10, 11, 12] and combinations of these [13, 14, 15]. These tests provide a probabilistic answer to the question: “Is this integer prime?” Although an affirmative answer is not 100% certain, it is answered fast and reliable enough for “industrial” use [16]. For speed, these various PRP tests are usually preceded by factoring methods such as sieving and trial division. The speed of the PRP tests depends on how quickly multiplication and modular reduction can be computed during exponentiation. Techniques such as Karatsuba’s algorithm [17, section 9.5.1], Toom-Cook multiplication, Fourier Transform algorithms [17, section 9.5.2] and Montgomery expo- nentiation [17, section 9.2.1] play their roles for different integer sizes. The sizes of the bases used are also critical. Oliver Atkin introduced the concept of a “Selfridge Unit” [18], approximately equal to the running time of a Fermat PRP test, which is called a selfridge in this paper. The Baillie-PSW test costs 1+3 selfridges, the use of which is very efficient when processing a candidate prime list. There is no known Baillie-PSW pseudoprime but Greene and Chen give a way to construct some similar counterexam- ples [19]. -
Note to Users
NOTE TO USERS This reproduction is the best copy available. UMI A SURVEY OF RESULTS ON GIUGA'S CONJECTURE AND RELATED CONJECTURES by Joseph R. Hobart BSc., University of Northern British Columbia, 2004 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in MATHEMATICAL, COMPUTER AND PHYSICAL SCIENCES (MATHEMATICS) THE UNIVERSITY OF NORTHERN BRITISH COLUMBIA July 2005 © Joseph R. Hobart, 2005 Library and Bibliothèque et 1 ^ 1 Archives Canada Archives Canada Published Heritage Direction du Branch Patrimoine de l'édition 395 Wellington Street 395, rue Wellington Ottawa ON K1A0N4 Ottawa ON K1A0N4 Canada Canada Your file Votre référence ISBN: 978-0-494-28392-9 Our file Notre référence ISBN: 978-0-494-28392-9 NOTICE: AVIS: The author has granted a non L'auteur a accordé une licence non exclusive exclusive license allowing Library permettant à la Bibliothèque et Archives and Archives Canada to reproduce,Canada de reproduire, publier, archiver, publish, archive, preserve, conserve,sauvegarder, conserver, transmettre au public communicate to the public by par télécommunication ou par l'Internet, prêter, telecommunication or on the Internet,distribuer et vendre des thèses partout dans loan, distribute and sell theses le monde, à des fins commerciales ou autres, worldwide, for commercial or non sur support microforme, papier, électronique commercial purposes, in microform,et/ou autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriété du droit d'auteur ownership and moral rights in et des droits moraux qui protège cette thèse. this thesis. Neither the thesis Ni la thèse ni des extraits substantiels de nor substantial extracts from it celle-ci ne doivent être imprimés ou autrement may be printed or otherwise reproduits sans son autorisation. -
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.