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POWER FIBONACCI SEQUENCES 1. Introduction Let G Be a Bi-Infinite Integer Sequence Satisfying the Recurrence Relation G If
POWER FIBONACCI SEQUENCES JOSHUA IDE AND MARC S. RENAULT Abstract. We examine integer sequences G satisfying the Fibonacci recurrence relation 2 3 Gn = Gn−1 + Gn−2 that also have the property that G ≡ 1; a; a ; a ;::: (mod m) for some modulus m. We determine those moduli m for which these power Fibonacci sequences exist and the number of such sequences for a given m. We also provide formulas for the periods of these sequences, based on the period of the Fibonacci sequence F modulo m. Finally, we establish certain sequence/subsequence relationships between power Fibonacci sequences. 1. Introduction Let G be a bi-infinite integer sequence satisfying the recurrence relation Gn = Gn−1 +Gn−2. If G ≡ 1; a; a2; a3;::: (mod m) for some modulus m, then we will call G a power Fibonacci sequence modulo m. Example 1.1. Modulo m = 11, there are two power Fibonacci sequences: 1; 8; 9; 6; 4; 10; 3; 2; 5; 7; 1; 8 ::: and 1; 4; 5; 9; 3; 1; 4;::: Curiously, the second is a subsequence of the first. For modulo 5 there is only one such sequence (1; 3; 4; 2; 1; 3; :::), for modulo 10 there are no such sequences, and for modulo 209 there are four of these sequences. In the next section, Theorem 2.1 will demonstrate that 209 = 11·19 is the smallest modulus with more than two power Fibonacci sequences. In this note we will determine those moduli for which power Fibonacci sequences exist, and how many power Fibonacci sequences there are for a given modulus. -
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. -
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. -
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. -
Sieving for Twin Smooth Integers with Solutions to the Prouhet-Tarry-Escott Problem
Sieving for twin smooth integers with solutions to the Prouhet-Tarry-Escott problem Craig Costello1, Michael Meyer2;3, and Michael Naehrig1 1 Microsoft Research, Redmond, WA, USA fcraigco,[email protected] 2 University of Applied Sciences Wiesbaden, Germany 3 University of W¨urzburg,Germany [email protected] Abstract. We give a sieving algorithm for finding pairs of consecutive smooth numbers that utilizes solutions to the Prouhet-Tarry-Escott (PTE) problem. Any such solution induces two degree-n polynomials, a(x) and b(x), that differ by a constant integer C and completely split into linear factors in Z[x]. It follows that for any ` 2 Z such that a(`) ≡ b(`) ≡ 0 mod C, the two integers a(`)=C and b(`)=C differ by 1 and necessarily contain n factors of roughly the same size. For a fixed smoothness bound B, restricting the search to pairs of integers that are parameterized in this way increases the probability that they are B-smooth. Our algorithm combines a simple sieve with parametrizations given by a collection of solutions to the PTE problem. The motivation for finding large twin smooth integers lies in their application to compact isogeny-based post-quantum protocols. The recent key exchange scheme B-SIDH and the recent digital signature scheme SQISign both require large primes that lie between two smooth integers; finding such a prime can be seen as a special case of finding twin smooth integers under the additional stipulation that their sum is a prime p. When searching for cryptographic parameters with 2240 ≤ p < 2256, an implementation of our sieve found primes p where p + 1 and p − 1 are 215-smooth; the smoothest prior parameters had a similar sized prime for which p−1 and p+1 were 219-smooth. -
Perfect Gaussian Integer Sequences of Arbitrary Length Soo-Chang Pei∗ and Kuo-Wei Chang† National Taiwan University∗ and Chunghwa Telecom†
Perfect Gaussian Integer Sequences of Arbitrary Length Soo-Chang Pei∗ and Kuo-Wei Chang† National Taiwan University∗ and Chunghwa Telecom† m Objectives N = p or N = p using Legendre N = pq where p and q are coprime Conclusion sequence and Gauss sum To construct perfect Gaussian integer sequences Simple zero padding method: We propose several methods to generate zero au- of arbitrary length N: Legendre symbol is defined as 1) Take a ZAC from p and q. tocorrelation sequences in Gaussian integer. If the 8 2 2) Interpolate q − 1 and p − 1 zeros to these signals sequence length is prime number, we can use Leg- • Perfect sequences are sequences with zero > 1, if ∃x, x ≡ n(mod N) n ! > autocorrelation. = < 0, n ≡ 0(mod N) to get two signals of length N. endre symbol and provide more degree of freedom N > 3) Convolution these two signals, then we get a than Yang’s method. If the sequence is composite, • Gaussian integer is a number in the form :> −1, otherwise. ZAC. we develop a general method to construct ZAC se- a + bi where a and b are integer. And the Gauss sum is defined as N−1 ! Using the idea of prime-factor algorithm: quences. Zero padding is one of the special cases of • A Perfect Gaussian sequence is a perfect X n −2πikn/N G(k) = e Recall that DFT of size N = N1N2 can be done by this method, and it is very easy to implement. sequence that each value in the sequence is a n=0 N taking DFT of size N1 and N2 seperately[14]. -
Arxiv:1804.04198V1 [Math.NT] 6 Apr 2018 Rm-Iesequence, Prime-Like Hoy H Function the Theory
CURIOUS CONJECTURES ON THE DISTRIBUTION OF PRIMES AMONG THE SUMS OF THE FIRST 2n PRIMES ROMEO MESTROVIˇ C´ ∞ ABSTRACT. Let pn be nth prime, and let (Sn)n=1 := (Sn) be the sequence of the sums 2n of the first 2n consecutive primes, that is, Sn = k=1 pk with n = 1, 2,.... Heuristic arguments supported by the corresponding computational results suggest that the primes P are distributed among sequence (Sn) in the same way that they are distributed among positive integers. In other words, taking into account the Prime Number Theorem, this assertion is equivalent to # p : p is a prime and p = Sk for some k with 1 k n { ≤ ≤ } log n # p : p is a prime and p = k for some k with 1 k n as n , ∼ { ≤ ≤ }∼ n → ∞ where S denotes the cardinality of a set S. Under the assumption that this assertion is | | true (Conjecture 3.3), we say that (Sn) satisfies the Restricted Prime Number Theorem. Motivated by this, in Sections 1 and 2 we give some definitions, results and examples concerning the generalization of the prime counting function π(x) to increasing positive integer sequences. The remainder of the paper (Sections 3-7) is devoted to the study of mentioned se- quence (Sn). Namely, we propose several conjectures and we prove their consequences concerning the distribution of primes in the sequence (Sn). These conjectures are mainly motivated by the Prime Number Theorem, some heuristic arguments and related computational results. Several consequences of these conjectures are also established. 1. INTRODUCTION, MOTIVATION AND PRELIMINARIES An extremely difficult problem in number theory is the distribution of the primes among the natural numbers. -
Some Combinatorics of Factorial Base Representations
1 2 Journal of Integer Sequences, Vol. 23 (2020), 3 Article 20.3.3 47 6 23 11 Some Combinatorics of Factorial Base Representations Tyler Ball Joanne Beckford Clover Park High School University of Pennsylvania Lakewood, WA 98499 Philadelphia, PA 19104 USA USA [email protected] [email protected] Paul Dalenberg Tom Edgar Department of Mathematics Department of Mathematics Oregon State University Pacific Lutheran University Corvallis, OR 97331 Tacoma, WA 98447 USA USA [email protected] [email protected] Tina Rajabi University of Washington Seattle, WA 98195 USA [email protected] Abstract Every non-negative integer can be written using the factorial base representation. We explore certain combinatorial structures arising from the arithmetic of these rep- resentations. In particular, we investigate the sum-of-digits function, carry sequences, and a partial order referred to as digital dominance. Finally, we describe an analog of a classical theorem due to Kummer that relates the combinatorial objects of interest by constructing a variety of new integer sequences. 1 1 Introduction Kummer’s theorem famously draws a connection between the traditional addition algorithm of base-p representations of integers and the prime factorization of binomial coefficients. Theorem 1 (Kummer). Let n, m, and p all be natural numbers with p prime. Then the n+m exponent of the largest power of p dividing n is the sum of the carries when adding the base-p representations of n and m. Ball et al. [2] define a new class of generalized binomial coefficients that allow them to extend Kummer’s theorem to base-b representations when b is not prime, and they discuss connections between base-b representations and a certain partial order, known as the base-b (digital) dominance order. -
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. -
Sieving by Large Integers and Covering Systems of Congruences
JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETY Volume 20, Number 2, April 2007, Pages 495–517 S 0894-0347(06)00549-2 Article electronically published on September 19, 2006 SIEVING BY LARGE INTEGERS AND COVERING SYSTEMS OF CONGRUENCES MICHAEL FILASETA, KEVIN FORD, SERGEI KONYAGIN, CARL POMERANCE, AND GANG YU 1. Introduction Notice that every integer n satisfies at least one of the congruences n ≡ 0(mod2),n≡ 0(mod3),n≡ 1(mod4),n≡ 1(mod6),n≡ 11 (mod 12). A finite set of congruences, where each integer satisfies at least one them, is called a covering system. A famous problem of Erd˝os from 1950 [4] is to determine whether for every N there is a covering system with distinct moduli greater than N.In other words, can the minimum modulus in a covering system with distinct moduli be arbitrarily large? In regards to this problem, Erd˝os writes in [6], “This is perhaps my favourite problem.” It is easy to see that in a covering system, the reciprocal sum of the moduli is at least 1. Examples with distinct moduli are known with least modulus 2, 3, and 4, where this reciprocal sum can be arbitrarily close to 1; see [10], §F13. Erd˝os and Selfridge [5] conjectured that this fails for all large enough choices of the least modulus. In fact, they made the following much stronger conjecture. Conjecture 1. For any number B,thereisanumberNB,suchthatinacovering system with distinct moduli greater than NB, the sum of reciprocals of these moduli is greater than B. A version of Conjecture 1 also appears in [7]. -
My Favorite Integer Sequences
My Favorite Integer Sequences N. J. A. Sloane Information Sciences Research AT&T Shannon Lab Florham Park, NJ 07932-0971 USA Email: [email protected] Abstract. This paper gives a brief description of the author's database of integer sequences, now over 35 years old, together with a selection of a few of the most interesting sequences in the table. Many unsolved problems are mentioned. This paper was published (in a somewhat different form) in Sequences and their Applications (Proceedings of SETA '98), C. Ding, T. Helleseth and H. Niederreiter (editors), Springer-Verlag, London, 1999, pp. 103- 130. Enhanced pdf version prepared Apr 28, 2000. The paragraph on \Sorting by prefix reversal" on page 7 was revised Jan. 17, 2001. The paragraph on page 6 concerning sequence A1676 was corrected on Aug 02, 2002. 1 How it all began I started collecting integer sequences in December 1963, when I was a graduate student at Cornell University, working on perceptrons (or what are now called neural networks). Many graph-theoretic questions had arisen, one of the simplest of which was the following. Choose one of the nn−1 rooted labeled trees with n nodes at random, and pick a random node: what is its expected height above the root? To get an integer sequence, let an be the sum of the heights of all nodes in all trees, and let Wn = an=n. The first few values W1; W2; : : : are 0, 1, 8, 78, 944, 13800, 237432, : : :, a sequence engraved on my memory. I was able to calculate about ten terms, but n I needed to know how Wn grew in comparison with n , and it was impossible to guess this from so few terms. -
Rational Tree Morphisms and Transducer Integer Sequences: Definition and Examples
1 2 Journal of Integer Sequences, Vol. 10 (2007), 3 Article 07.4.3 47 6 23 11 Rational Tree Morphisms and Transducer Integer Sequences: Definition and Examples Zoran Suni´cˇ 1 Department of Mathematics Texas A&M University College Station, TX 77843-3368 USA [email protected] Abstract The notion of transducer integer sequences is considered through a series of ex- amples (the chosen examples are related to the Tower of Hanoi problem on 3 pegs). By definition, transducer integer sequences are integer sequences produced, under a suitable interpretation, by finite transducers encoding rational tree morphisms (length and prefix preserving transformations of words that have only finitely many distinct sections). 1 Introduction It is known from the work of Allouche, B´etr´ema, and Shallit (see [1, 2]) that a squarefree sequence on 6 letters can be obtained by encoding the optimal solution to the standard Tower of Hanoi problem on 3 pegs by an automaton on six states. Roughly speaking, after reading the binary representation of the number i as input word, the automaton ends in one of the 6 states. These states represent the six possible moves between the three pegs; if the automaton ends in state qxy, this means that the one needs to move the top disk from peg x to peg y in step i of the optimal solution. The obtained sequence over the 6-letter alphabet { qxy | 0 ≤ x,y ≤ 2, x =6 y } is an example of an automatic sequence. We choose to work with a slightly different type of automata, which under a suitable interpretation, produce integer sequences in the output.