1. Polynomial Reachability in Modular Arithmetic Arturo Rosas, Chris Jones

2. Rings • In linear algebra, a ring is a nonempty set in which the following are true: • Addition and multiplication are defined • Associativity,commutativity, and distributivity • The set contains an additive identity • Each element in the set has an additive inverse which is also in the set

3. Modular Arithmetic and Zn • Zn: a ring whose set is the integers [0,n-1] • Ex: Z4 contains {0, 1, 2, 3} • Addition is defined as (a + b) mod n • Multiplication is defined as (ab) mod n

4. Functions and Polynomials • f: Zn → Zn • Maps the elements of Zn to another element in Zn (possibly itself) • Example in Z4: f(0)=0, f(1)=1, f(2)=0, f(3)=1 • nn functions from Zn to itself, but only some can be represented by polynomials of the form f(x) = a0 + a1x + a2x2 + … + an-1xn-1 • The question (and objective) is how many? And which ones?

5. Applications in Computer Science • Cryptography • One major application is cryptanalysis • Can help determine weaknesses in keys

6. Attacks on RSA • Johan Hastad • Solving simultaneous modular equations of low degree • Coppersmith's Attack • Related Hastad’s work • Where do we come in? • Polynomial Collisions • Lower number of distinct polynomials for given n

7. Methodology • Vandermonde Matrix • Defined as follows • Useful in Interpolation problems

8. Slow Method • For every augment every value of f with the Vandermonde matrix as such: • Then use Gaussian Elimination to interpolate the coefficients of f(x)

9. Faster Method • Slow method uses A LOT of repeated operations • How can we exploit this? • Use Gaussian Elimination once • Generate a linear transformation τduring Gaussian Elimination such that: • Apply this transformation function to each column vector of f(x) values

10. How to parallelize? • Allocate each column vector of the Vandermonde matrix to a processee (be sure to use striping as in regular Gaussian Elimination):

11. How to parallelize? • After each successive row operation update tau. • K = total number of row operations to reduce the Vandermonde matrix • Once the Vandermonde matrix is in reduced form, we can interpolate each function in parallel

12. How to parallelize? • Once the Vandermonde matrix is in reduced form, we can interpolate each function in parallel. • Each processee will have a row vector of tau • From there each processee will compute their respective ‘s and after a simple MPI_Gather call can collect each value and reconstruct

13. Time complexities (sequential) • Sequential Gaussian elimination for each possible function (slow method): O(n3) * number of functions to check (nn for all of Zn) • Improved sequential algorithm: • Gaussian elimination with matrix multiplication at each step: O(n4) • Substitution w/ matrix multiplication at each step: O(n3) • Matrix-vector multiplication and solving for coefficients: O(n2) for each function • Total: O(n4) + O(n3) + O(n2) * number of functions to check (nn for all of Zn) = O(n2) * number of functions to check (nn for all of Zn)

14. Parallel Time when P = n • Parallel elimination w/ parallel matrix multiplication at each step: O(n3) • Parallel substitution w/ parallel matrix multiplication at each step: O(n3) • Parallel matrix-vector multiplication and solving for coefficients: O(n) for each function • Total: O(n3) + O(n3) + O(n) * number of functions to check (nn for all of Zn) = O(n) * number of functions to check (nn for all of Zn)

15. Speedup and Efficiency • Speedup = Tseq/Tpar = O(n2) * nn/ O(n) * nn = O(n2) / O(n) = n → linear • Efficiency = Speedup / P = n/P = n/n = n • This leads to the belief that it is cost optimal

16. Works Cited • Hastad, Johan. "Solving Simultaneous Modular Equations of Low Degree." SIAM Journal of Computing 17 (1988): 336-41. Print. • Capretta, Venanzio. "Mathematics for Computer Scientists." Web. <http://www.cs.nott.ac.uk/~vxc/g51mcs/ch06_combinatorics.pdf>.