Complexity of exponentiation is

Since x starts from 2 and we square at each step, it follows the following sequence: 2^ (2^0), 2^ (2^1), 2^ (2^2) 2^ (2^k), 2^ (2^k) * 2^ (2^k) = 2^ (2^ (k + 1)) The loop continues as long as 2^ (2^k) <= n => 2^k <= log (n) => k <= log (log (n)) share. Share a link to this answer. Copy link Arithmetic Complexity of Exponentiation. Ask Question Asked today. Active today. Viewed 3 times 0 $\begingroup$ I If I assume it to be k-operations then the computation für the exponentiation should be: $\sum_{k=1}^n k=\frac{n(n+1)}{2}$ For the multiplication: $\sum_{k=1}^n 1=n$ For the addition: $\sum_{k=1}^n 1=n$ So we come up with $\frac{n(n+1)}{2}+n+n=\mathcal{O}(n^2)$ Is that correct. Exponentiation is a mathematical operation, written as b n, involving two numbers, the base b and the exponent or power n, and pronounced as b raised to the power of n . When n is a positive integer, exponentiation corresponds to repeated multiplication of the base: that is, b n is the product of multiplying n bases

I am trying to analyse the time complexity of the fast exponentiation method, which is given as. xn = {xn 2. xn 2 if n is even x. xn − 1 if n is odd 1 if n=0. I tried to write it as, T(n) = {T(n 2). T(n 2) if n is even T(n − 1) if n is odd 1 if n=0 We now analyze the complexity of the fast exponentiation algorithm, that is, we determine how many operations are needed to compute a power with this algorithm. Theorem 15.3.10. Let \(\star\) be a binary operation on a set \(G\) and \(b\in G\text{.}\) Let \(m\in \N\) and \(n\in\N\) such that \(m\le 2^n\text{.}\) Then, fast exponentiation, \(\gexp{b}{m}{\star}\) can be computed in at most \(2\cdot n\) operations \(\star\text{.}\

algorithm - Time complexity of exponentiation - Stack Overflo

Also the graph of the exponentiation $\Gamma exp=\{(x,y,z) : x^y=z\}$ has low complexity. $\endgroup$ - Kaveh Sep 30 '10 at 13:27 3 $\begingroup$ Sadeq: If you want to avoid complexity classes, L is in no way better than EXP.. Strictly speaking, the Complexity of Matrix Exponential does not exist. Instead, you have an approximate calculation and you are asking about its complexity. Now, a few comments : there is not just one approximate exponential, but actually a sequence of approximations. Therefore, the complexity must involve the tolerance parameter. One cannot overlook the stability of the approximation. At. An algorithm is said to take linear time, or O(n) time, if its time complexity is O(n). Informally, this means that the running time increases at most linearly with the size of the input. More precisely, this means that there is a constant c such that the running time is at most cn for every input of size n Time Complexity of Fast Exponentiation is O (logn). You can easily find that in our above example where we have reduce a 10 step problem into 3 steps. I hope you have liked the article and thanks for reading!!!!! « Dictionary in Pytho

I don't know why the time complexity of the Fibonacci sequence is O(Fib(n)). So, I googled about it, he says. There's a thing that grows exactly at Fibonacci numbers. It's a horrible thing. You wouldn't want to do it. The reason why the time has to grow that way is because we're presuming in the model-- the substitution model that I gave you, which I'm not doing formally here, I sort of now. Solution: Simply raise the permutation to $k$-th power using binary exponentiation, and then apply it to the sequence. This will give you a time complexity of $O(n \log k)$. Note: This task can be solved more efficiently in linear time by building the permutation graph and considering each cycle independently. You could then compute $k$ modulo the size of the cycle and find the final position for each number which is part of this cycle Exponentiation Factorial The n-Queens Problem Module Home Page Title Page JJ II J I Page 3 of 15 Back Full Screen Close Quit • Suppose algorithm A's worst-case time complexity t A(n) =def n2, and algorithm B's worst-case time complexity t B(n) =def 2 n. 2 grows much much more quickly than n2. 0 50000 100000 150000 200000 0 20 40 60 80 100. Complexity of iterative exponentiationHelpful? Please support me on Patreon: https://www.patreon.com/roelvandepaarWith thanks & praise to God, and with than.. Understanding Time complexity of recursive functions - YouTube. Hi, in this video i will show how to analyse Time Complexity of a function with multiple recursion calls

Exponential Time complexity denotes an algorithm whose growth doubles with each additon to the input data set. If you know of other exponential growth patterns, this works in much the same way. The.. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.. Visit Stack Exchang In this tutorial, we will learn about Matrix Exponentiation and apply it to solve linear recurrences in one variable with C++. Given a linear recurrence relation in one variable along with the base cases, the task is to find the N th term in logarithmic time complexity. Since the N th term can be very large, we will compute it modulo 10 9 + 7 (a big prime number) This technique of raising a number to a large exponent is often used in competitive programming. We talk about how we can move from the brute force approach.

In mathematics and computer programming, exponentiating by squaring is a general method for fast computation of large positive integer powers of a number, or more generally of an element of a semigroup, like a polynomial or a square matrix.Some variants are commonly referred to as square-and-multiply algorithms or binary exponentiation.These can be of quite general use, for example in modular. We can say, Addition is to subtraction as exponentiation is to logarithm. We can also say, Multiplication is to division as exponentiation is to logarithm. With quadratic time complexity, we learned that n * n is n^2. If the value of n is to 2, then n^2 is equal to 4. It then follows that 2 to the third power, 2^3, is equal to 8 Given three numbers a, b and c, we need to find (a b) % c Now why do % c after exponentiation, because a b will be really large even for relatively small values of a, b and that is a problem because the data type of the language that we try to code the problem, will most probably not let us store such a large number. Examples: Input : a = 2312 b = 3434 c = 6789 Output : 6343 Input : a.

computer science - Arithmetic Complexity of Exponentiation

  1. Time Complexity Definition. Time complexity is the amount of time taken by an algorithm to run, as a function of the length of the input. It measures the time taken to execute each statement of code in an algorithm. Time Complexity Introduction. Space and Time define any physical object in the Universe. Similarly, Space and Time complexity can.
  2. Four The Complexity of Exponentiation 16 Points How hard is it to check if a from CS 103 at Massachusetts Institute of Technolog
  3. describe well-known exponentiation algorithms with their complexity analysis. Our algo-rithm introduces a new idea of solving the exponen tiation problem. We show cases in which the complexity of our algorithm is better tha n the complexity of any other algo-rithms. Introduction The problem of efficient exponentiation is extremel y important for the devel-opment of modern cryptography. Many.

The parallel complexity of exponentiating polynomials over finite fields The parallel complexity of exponentiating polynomials over finite fields Fich, Faith E.; Tompa, Martin 1988-06-01 00:00:00 Modular integer exponentiation (given a, e, and m , compute a e mod m ) is a fundamental problem in algebraic complexity for which no efficient parallel algorithm is known In this paper, we review their hardware complexity and propose original implementations of MM and ME that become useful for 24-bit operators (Karatsuba algorithm) or 373-bit operators (FFT algorithm) So let's look at the Wesolowski method and how it reduces the complexity of the zero-knowledge proof process. First, Peggy and Victor know three values u, w and x and where:. w=u^x. Peggy will prove to Victor that she still knows the values of u and x, based on a challenge.Initially, Victor generates an n-bit prime number (l) and passes it to Victor.. Victor then computes a quotient and a.

$\begingroup$ Maybe what I meant by 'binary search' in my second question wasn't clear. What I mean to ask is whether it is possible to quickly find the period r (without any quantum computation) by looking at the values of the modular exponentiation to powers of two, then based on the answers, selectively calculate other values of a^x (mod N) via a form of 'interval halving' until we can. If I just concatente the circuit of $\mathbf{U}$ to itself $2^i$ times, applying $\mathbf{U}^{2^i}$ is $2^i$ times as long as applying $\mathbf{U}$, which breaks the complexity. I've seen papers and posts about modular exponentiation, but I'm not sure this would work in my case, since I do not work modulo some whole number Complexity (number of possible characters in each position) Length (number of positions) The 'randomness' of a password is simple to calculate: complexity ^ length, where ^ is exponentiation. As you might know, changing the exponent (the length) makes the number much larger than changing the base (complexity). For example, a random password using 6 characters, consisting of a-z, A-Z, and 0-9. What's the computational complexity or RSA? One can assume it's O(prime length^2) it you consider multiplication by column, but speed tests slightly differ on slow operations with private key and differ a lot on public key. Why? > openssl speed rsa1024 rsa2048 rsa4096 sign verify sign/s verify/s rsa 1024 bits 0.002544s 0.000124s 393.1 8049.2 rsa 2048 bits 0.017023s 0.000467s 58.7 2140.7 rsa.

Exponentiation - Wikipedi

Using the exponentiation by squaring one it took 3.9 seconds. We can also treat the case where b is odd by re-writing it as a^b = a * a^(b-1), and break the treatment of even powers in two steps. This makes the algorithm easier to understand and a bit more efficient (surprisingly). The algorithm below took 3.4 seconds to complete the same task as above. int expo(int a, int b){ int result; if. The Complexity of Certain Multi-Exponentiation exponentiation algorithms with representations of the exponents. We are especially interested in the case where the inversion of group elements is fast: this is true for example for elliptic curves, groups of rational divisor classes of hyperelliptic curves, trace zero varieties and XTR. The methods can also be used for computing single ex. Request PDF | The Complexity of Certain Multi-Exponentiation Techniques in Cryptography | We describe, analyze and compare some combinations of multi-exponentiation algorithms with representations. We describe, analyze and compare some combinations of multi-exponentiation algorithms with representations of the exponents. We are especially interested in the case where the inversion of group elements is fast: this is true for example for elliptic curves, groups of rational divisor classes of hyperelliptic curves, trace zero varieties and XTR Efficient Secure Two-Party Exponentiation Ching-Hua Yu1, Sherman S.M. Chow2, Kai-Min Chung3, and Feng-Hao However, they did not explicitly analyze the complexity of their solution in this setting. Their construction is based on the existence of secure multiplication on linear shares over Z Q, so it works for both multi-party with honest majority and two-party. A simpler setting where only.

asymptotics - Time complexity of the fast exponentiation

To recap time complexity estimates how an algorithm performs regardless of the kind of machine it runs on. You can get the time complexity by counting the number of operations performed by your code. This time complexity is defined as a function of the input size n using Big-O notation. n indicates the input size, while O is the worst-case scenario growth rate function. We use the Big-O. Hardware Complexity of Modular Multiplication and Exponentiation Abstract: Large integer modular multiplication (MM) and modular exponentiation (ME) are the foundation of most public-key cryptosystems, specifically RSA, Diffie-Helleman, EIGamal, and the elliptic curve cryptosystems. Thus, MM algorithms have been studied widely and extensively. Most of the work is based on the well-known. So, collectively, the time complexity of all the statements can be considered as O(1). Now each of this statement will be executed Log (n) number of times. Thus, the time complexity is generally measured in terms of the highest degree of complexity based on the size of the input View Homework Help - Binary Exponentiation.cpp from CS 2120 at Western University. /Binary exponentiation is a technique to calculate a^n in O(logn) time complexity. / Implementation: / int power(in

Fast Exponentiation - UNC

  1. sion, and exponentiation over the integers and/or the nite elds. More e cient protocols for these basic operations can result in an more e cient protocol for f. Indeed, a signi cant e ort has focused on designing protocols for these op- erations. For examples, Ishai, Prabhakaran, and Sahai [10,11] studied general solutions for secure arithmetic computations over rings, which correspond to.
  2. Its time complexity is simply 1 because it doesn't matter how many letters are in the list, it will always take just one operation. O(1) is the best possible time complexity! Data structures like hash tables make clever use of algorithms to pull off constant time operations and speed things up dramatically. Linear time or O(n) If your program does something like duplicate all the letters.
  3. proposed exponentiation with Montgomery modular multiplications adapted to the size of the operands. We analyze the complexity of this approach: Table I, below, contains the basic cost per loop turn of an exponentiation. We notice that the proposed approach always reach the best complexity while having the higher security level compare
  4. In this paper, an efficient technique for parallel computation of the modular exponentiation is proposed and our algorithm can reduce time complexity. We can have the speedup ratio as 1.06 or even 2.75 if the proposed technique is used. In Savas-Tenca-Koc algorithm, they design a multiplier with an insignificant increase in chip area (about 2.8%) and no increase in time delay. Our proposed.
  5. C++ Program of Fast Exponentiation using Bit Manipulation. By Anushka Sethi. This project is performed in C++ and helps students and users to find Fast Exponentiation of a number using the method of Bit Manipulation. Today we will learn the Bit Manipulation method to find the Fast Exponentiation of a number using C++. Given two integers a and n, the task is to calculate a raised to power n (i.
  6. Hence, I always use this method when I have to find Modular Exponentiation. The code may seem a little confusing, so feel free to ask questions. When I first got my hands on this code, I had no idea how it worked. I found it in a forum with a title, Faster Approach to Modular Exponentiation. Since then I have been using this code. Resource
  7. Complexity Results for Security Protocols exponentiation symbol, a restriction not present in our model. Structure of the paper. In Section 2, we introduce our protocol and intruder model, including the oracle rules mentioned above. The NP-completeness result for this general framework is proven in Section 3. In Section 4 and 5, we instantiate the oracle rules by rules that allow the.

Exponentiation by squaring - Wikipedi

What is the time complexity for decryption of RSA encryption if the decryption key is known? And what is it for the case when it is not known? I need it in Big-O notation with its derivation please. 6 comments. share. save. hide. report. 31% Upvoted. This thread is archived. New comments cannot be posted and votes cannot be cast. Sort by. best. level 1. 1 year ago. I need it in do-my-homework. 1 The benchmarks and complexity analysis are done for randomly chosen bit integers and . In particular, with probability , the exponentiation will wrap around in bit space; that is, as natural numbers, and we are only computing its value mod 1. Определения, обсуждения, формулировка основного результата В работе исследуется возможность уточнения нижней оценки для известной (см., например, [1, разд. 4.6.3]) задачи о сложности возведения в степень, т. е. задачи о.

Binary exponentiation (Power in log N

Algorithm of lesser complexity can be utilized in this case -- we will see O(log n) algorithm next. This is the reason why most of the time (a^n)%(some number) is to be calculated. Most of the time that some number is 1e9 + 7 (which is a prime) in competitive programming problems. Binary Exponentiation Approach: O(log n) For achieving O(log n) complexity, the mathematical fact that any. We can reduce time complexity to by using what is called as exponentiation by squaring. Exponentiation by squaring. Basic idea of exponentiation by squaring is. If power is 0, algorithm should return 1. It is the base case, other cases will built on it. Other than it, at each step we are dividing the exponent by two and square the base, and then for the case where the exponent is odd you. For example, number $72011021110$ is divisible by 7, because $-72+11-21+110=28$ is divisible by 7. This approach can be found in book Teoria Liczb (The Theory of Numbers) by none other than the extraordinary Polish mathematician Wacław Sierpiński.His method works for all three divisors of $1001=7\times11\times13$ Modular exponentiation is a type of exponentiation performed over a modulus. It is useful in computer science, especially in the field of public-key cryptography. The operation of modular exponentiation calculates the remainder when an integer b (the base) raised to the eth power (the exponent), be, is divided by a positive integer m (the modulus) Complexity of the function is O(n) and hence cannot find required answer for large values of b or n(for n values > 10 8) in reasonable time. This can be optimised using Modular Exponentiation Algorithm. This algo solves the problem in O(log n) complexity. Here is the function

algorithms - Modular exponentiation running time

Computational complexity of mathematical operations

Exponentiation with minimum number of multiplication by

Exponentiation - Time Complexity analysis of recursion

and private exponentiation, which are four main applications of bit-decomposition. However, when considering perfect security, bit-decomposition does not have a linear communication complexity. Thus any protocols involving bit-decomposition inherit this ine ciency, i.e. the communication complexity is non-linear. Constructing protocols for MPC problems without relying on bit-decomposition is a. perform modular exponentiation with very long integers. This operation is at the heart of many practical public-key algorithms such as RSA and discrete logarithm schemes. We combine the Montgomery modular multiplication algorithm with a new systolic array design, which is capable of processing a variable Radix-4 modular multiplication and exponentiation algorithms for the rsa public-key. The complexity of in relies altogether upon what L is. e in L will become L.__contains__(e).. See, this time complexity archive for the intricacy of a few implicit sorts.. Here, Look at the summary of in. list - Average: O(n) set/dict - Average: O(1), Worst: O(n) The O(n) most pessimistic scenario for sets and dicts is phenomenal, yet it can occur if __hash__ is carried out inadequately Complexity of Arithmetic: Exponentiation. 1-Example: b = 49. Then k = 4 and b = 11001. That's better than O(b log2(m)) If we want to be super precise, computing a mod mtakes another O(log(a)*log(m)) time. Exponentiation over integers (without mod) Input: two positive integers a, b. Output: ab (over integers) Naïve algorithm . result = 1. for i=1b. result = result * a. return result.

Table 1. Complexity comparison among various modular multi-exponentiation al-gorithms when l = 2. k is the larger bit length of the two exponents. Our Results. We show that the expected number of MMs that the WLLC algo-rithm [15] can achieve is actually 1.556k rather than 1.306k. This result is shown b Time Complexity of above solution is O(Log y). Modular exponentiation (Recursive) This article is contributed by Shivam Agrawal. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above. Attention reader! Don't stop learning now

cc.complexity theory - Complexity of exponential function ..

  1. computational complexity of the modular exponentiation is decreased. In [16] CMM method in [15] is used in multiplication phase. Wu in [17] proposed divide the signed-digit exponent into three equal lengths and use of CMM technique in order to compute common part of multiplications, once rather than several times. In this paper, a new Montgomery modular multiplication algorithm based on.
  2. imum complexity of a term t(~x) that computes a given function f: Nn→ N for all n-tuples of num-bers ≤ N. We formulate and prove a whole array of further results, including in Section 7 the optimality of the binary gcd algorithm among algorithms that only allow Presburger operations as arithmetic primitives, and in Section 9 a log.
  3. mathematical challenge, number theory, A005245, integer complexity, complexity theory, exponentiation, mathematics is inconsistent, arithmetic is inconsistent, inconsistency. My personal page. Any comments are welcome: Karlis.Podnieks@lu.lv. Mathematical Challenge. By Karlis Podnieks. University of Latvia . Number Theory. Consider representing of natural numbers by using 1, +, * and brackets.

linear algebra - What is the time complexity of the matrix

Time complexity - Wikipedi

  1. Without recursive comprehension, the complexity of definitions of functions is characterized by two parameters: (1) the complexity of evaluating the corresponding predicate (2) the permitted growth rate. High (2) leads to high (1). However, in a weak theory, (2) has to be very limited, so (1) is important to allow definition of complex functions. Without total exponentiation, even with.
  2. Rabbits are incredible animals. One of their more interesting characteristics is related with their reproduction. If we keep a couple of adult rabbits in optimal conditions of life, it is scientifically proved that, each month, that couple is capable of procreating a new couple of young rabbits
  3. g languages have a built-in implementation of exponentiation. Task . Re-implement integer exponentiation for both int int and float int as both a procedure, and an operator (if your language supports operator definition). If the language supports.
  4. Creating sixteen squares requires sixteen operations. But our second, refactored and optimized, approach is O(log n), or logarithmic time (the inverse of exponentiation). Creating sixteen squares requires only four steps. We'll look at O(log n) later. Let's begin with O(1), which will help us understand O(n). O(1): Constant Time Complexity
  5. Matrix exponentiation (fast) It should be a constant factor faster than matrix exponentiation, but the asymptotic time complexity is still the same. Summary: The two fast Fibonacci algorithms are matrix exponentiation and fast doubling, each having an asymptotic complexity of \(Θ(\log n)\) bigint arithmetic operations. Both algorithms use multiplication, so they become even faster when.
  6. Discrete Logarithm. The discrete logarithm is an integer $x$ satisfying the equation $$a^x \equiv b \pmod m$$ for given integers $a$, $b$ and $m$. The discrete.

Fast Exponentiation in Python - CodeSpeed

Assessment of calculating complexity of the represented procedure of the exponentiation in Galois fields can be performed by counting the needed operations of XOR and shifts. The exponentiation procedure consists of m cycles, in each of which the operation of squaring in Galois field is performed and, with probability of 0.5 - multiplication on constant A (if considering appearing of 0 and 1. Modular Exponentiation is one of the basic technique used to compute efficiently ( a b ) % c where a,b,c are positive integers. A straightforward algorithm to do the task can be to iteratively multiply the result with 'a' and take the remainder with 'c' at each step time-complexity classes, we give a table that vividly illustrates how the time an algorithm takes determines the sizes of input that can be feasibly handled by the algorithm. O(1). Constant time. Algorithms whose running time does not depend on the size of the input; there is an upper bound on the number of basic steps executed. Examples: Finding the maximum of two integers. Also, the. This complexity analysis of the naive exponentiation algorithm also holds for the naive exponentiation algorithm for integers, Algorithm 2.6.1. In Chapter 2, we gave two algorithms for computing \(c^4\) for some integer \(c\text{,}\) namely Algorithm 2.2.3 and Algorithm 2.4.3.Although the output of both algorithms was the same, the number of multiplications to compute the output differed

Is the time complexity of the Fibonacci sequence O(fib(n

Permitted operators: +,-, *, /, ^(exponentiation) Blanks are permitted in the expression; Parenthesis are permitted ; Prefix and Postfix expressions can be evaluated faster in comparison to an infix expression because we don't need to process any brackets or follow the operator precedence rule. In postfix and prefix expressions whichever operator comes before will be evaluated first. Algorithms and Data Structures implemented in Java - lcfme/java-algorithms-implementatio

Introduction to Algorithm Complexity Analysis and Big-OModular Exponentiation – Nadide PasaliA coffee-break introduction to time complexity of

Binary Exponentiation - Competitive Programming Algorithm

Exponentiation By Squaring. So , today we are going to learn about exponentiation by squaring. We all know simple exponentiation , Right? Suppose we want to calculate exp(2,4) , then we can calculate the ans by simply initialising ans = 1 and then multiply ans with base( here base is 2) from 1 to power times (here power is 4). So we get ans = 16. But if you observe closely the time complexity. Low complexity architecture for exponentiation in GF(2 m) Author(s): M.A. Hasan and V.K. Bhargava; DOI: 10.1049/el:19921272; For access to this article, please select a purchase option: Buy article PDF. £12.50 (plus tax if applicable) Add to cart. Buy Knowledge Pack. 10 articles for £75.00 (plus taxes if applicable) Add to cart. IET members benefit from discounts to all IET publications and.

The computational complexity of our MCMC method grows as the size of the CTMC state space squared. Moreover, in contrast to competing matrix exponentiation methods, if the rate matrix is sparse, we can leverage this sparsity and increase the computational efficiency of our algorithm further. Using simulated data, we illustrate advantages of our. The complexity function defined here has a better fit vs. the execution time when compared to the EIP-198 complexity function. This better fit is because this complexity formula accounts for the use of binary exponentiation algorithms that are used by 'bigint' libraries for large exponents. You may also notice the regression line of the proposed complexity function bisects the test vector. matrix exponentiation and results in an algorithm with complexity O(s2). Moreover, the method of Rao and Teh (2011) can further increase its computational efficiency by taking advantage of sparsity of the CTMC rate matrix. Here, we take the method of Rao and Teh (2011) and extend it to phylogenetic stochastic mapping Kochergin VV and Kochergin DV (2017), IMPROVEMENT OF THE LOWER BOUND FOR THE COMPLEXITY OF EXPONENTIATION, Prikladnaya diskretnaya matematika., dec, 2017. (38), pp. 119-132. Tomsk State University. Altman HJ (2014), Integer complexity, addition chains, and well-ordering. Thesis at: University of Michigan. Abbas M and Gustafsson O (2012), Integer Linear Programming Modeling of Addition.

c++ - Effectively calculate the result of geometric seriesKoç Lab - Finite Field ArithmeticProgrammers ArmyEuler&#39;s Totient FunctionLibra (the other one): A succinct Zero-Knowledge ProofsPPT - Probabilistic Polynomial-Time Process Calculus forHistogram comparison chart of P-values for two randomPPT - 22C:19 Discrete Math Integers and Modular Arithmetic

fast evaluate modular exponentiation which combines a binary exponentiation method, a complement representation method, and a signed-digit representation method. Because modular exponentiation is one of the most time-consuming operations for many cryptosystems, we adopt the proposed method to reduce the computational complexity from 11 8 k to 13 12 k, where k is the bit-length of the exponent. The Complexity of Cert... × Publication title. Copy citation to your local clipboard. close. copy delete add this publication to your clipboard. community post; history of this post; URL; DOI; BibTeX; EndNote; APA; Chicago; DIN 1505; Harvard; MSOffice XML; The Complexity of Certain Multi-Exponentiation Techniques in Cryptography. R. Avanzi. J. Cryptol. 18 (4): 357-373 (2005) Links and. The shortest addition chains can provide high efficiency for the modular exponentiation in RSA, but the time and space complexity of finding out the shortest addition chains is extremely high. This research adopts addition chains tree to produce addition chains and store them into a database,in which the number of child nodes of each node is uncertain until the children of this node in the. Abstract: Modular exponentiation is fundamental to several public-key cryptography systems such as the RSA encryption system, as well as the most dominant part of the computation performed. The operation is time consuming for large operands. This paper analyses and compares the complexity of a variety of algorithms proposed to compute the modular exponentiation of a relatively large binary. In Table 5, we show the complexity of encryption and verification of batch Cramer-Shoup cryptosystem and the normal Cramer-Shoup implementation (with existing simple multi-exponentiation approaches), respectively. The reason why we compare the BBM algorithm and simple multi-exponentiation algorithm is that they need the least memory and can be used everywhere, if the storage is enough.

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