README.md

Baby Step Giant Step Algorithm

Prerequisites:

1. Cyclic Groups

2. Discrete Logarithm Problem

A method to reduce the time complexity of solving DLP is to use Baby Step Giant Step Algorithm. While brute forcing DLP takes polynomial time of the order , Baby Step Giant Step Algorithm can compute the value of x in polynomial time complexity. Here, n is the order of the group.

This algorithm is a tradeoff between time and space complexity, as we will see when we discuss the algorithm.

Algorithm

x can be expressed as x = i*m + j, where and 0 <= i,j < m.

Hence, we have:

We can now use the above property for solving DLP as follows:

1. Iterate j in its range and store all values of with corresponding values of j in a lookup table.

2. Run through each possible iteration of i and check if exists in the table (ie. check if == ).

   • If it does then return i*m + j as the value of x

   • Else, continue

Shortcomings

Although the algorithm is more efficient as compared to plain brute-forcing, other algorithms of the same time complexity (Pollard's rho) are used for solving DLPs because of the fact that storing the look up table requires quite a lot of space.

Implementation

I wrote an implementation of the above algorithm in python/sage:

from sage.all import *

def bsgs(g, y, p):
    mod_size = len(bin(p-1)[2:])

    print "[+] Using BSGS algorithm to solve DLP"
    print "[+] Modulus size: " + str(mod_size) + ". Warning! BSGS not space efficient\n"

    m = ceil(sqrt(p-1))
    # Baby Step
    lookup_table = {pow(g, j, p): j for j in range(m)}
    # Giant Step pre-computation
    c = pow(g, m*(p-2), p)
    # Giant Steps
    for i in range(m):
        temp = (y*pow(c, i, p)) % p
        if temp in lookup_table:
            # x found
            return i*m + lookup_table[temp]
    return None

You can check out the complete code here: bsgs.py

Resources & References

1. Rahul Sridhar- Survey of Discrete Log Algorithms