This post is part of a series on Cryptography 101, source code is available at elbaulp/cripto, there is also a Table of Contents.

# Modular Exponentiation

Given a, m and n now you want to compute $$a^m\bmod n$$ in a efficient way, to do it, let first define Fermat-Euler Theorem:

Given $$a,n \in \mathbb N$$, if $$gcd(a,n) = 1$$, $$a^{\phi(n)} \equiv 1\pmod n$$

Here are some examples. In $$\mathbb Z_5,\ \phi(5) = 4$$, Fermat-Euler theorem says then that $$1^{4} = 2^{4} = 3^{4} = 4^{4} = 1$$. In $$\mathbb Z_{53}, \phi(53) = 52$$, in order to compute $$7^{111}$$, as $$gcd(7, 53) = 1$$ then $$7^{52} = 1$$, so $$7^{52\cdot 2} = 7^{104} = 1$$ and therefore $$7^{111} = 7^7 = 29$$.

A particular case of Fermat-Euler Theorem is Fermat's Little Theorem:

Given p prime, $$a \in \mathbb N:\ 1 \leq a \leq p-1$$ then $$a^{p-1} \equiv 1\pmod p$$

As consequence, now you have $$a^{p} \equiv a\pmod p$$. Let see it with more examples:

$$\mathbb Z_4$$ units are $$\mathcal U(\mathbb Z_4) = \{1,3\}$$, it has only two units because $$\phi(4) = \phi(2^2) = 2$$, particularly those units are 1 and 3, because they satisfy $$1^2 = 3^2 = 1$$. Above you saw that in $$\mathbb Z_5,\ \phi(5) = 4$$, and therefore all its elements have inverse. Lets check one of Fermat's Little theorem variants also holds in $$\mathbb Z_5$$. The theorem states $$a^{p} \equiv a\pmod p$$, and as you can see, in $$Z_5, 0^5 = 0, 1^5 = 1, 2^5 = 2, 3^5 = 3, 4^5 = 4$$.

## An algorithm to compute modular exponentiation

There is more than one algorithm to compute modular exponentiation, here is one:

• Input: $$a\in\mathbb Z_n$$, an integer $$0 \leq k \lt n$$ which binary representation is $$\sum_{i=0}^t k_i 2^i$$.
• Output: $$a^k \pmod n$$
  1. Set b = 1, If k = 0 return b.
2. A = a
3. If k_0 = 1 then b = a
4. For each i from 1 to t:
1. A = A * A modulo n.
2. If k_i = 1 then b = A * b modulo n
5. return b


This algorithm is based in the fact that the exponent can be represented in binary. The binary representation of k is given by $$\sum_{i=0}^t k_i 2^i$$, in which each $$k_i\in \{0, 1\}$$. It is possible this representation does not sound familiar to you, but is nothing more that an abbreviated form of the more common one, for example, lets represent 5 in binary, which is $$1\cdot 2^0 + 0\cdot 2^1 + 1\cdot 2^2$$. Knowing this, then:

$$a^k = \prod_{i=0}^t a^{k_i 2^i} = (a^{2^0})^{k_0}(a^{2^1})^{k_1}\cdots(a^{2^t})^{k_t}$$

If you analyze the expression above, when $$k_i = 0$$, the term $$(a^{2^i})^{k_i} = 1$$, which implies this term is not going to change the final result, because is multiplying by 1.

Now reading the algorithm becomes easier. It takes an integer and other two numbers, k,n > 0 and computes $$a^{k} \pmod n$$. If k==0 is not necessary compute anything and it returns 1, because any number to the power of 0 is 1. In step 3, if $$k_0$$ (the least significant bit) is 1, therefore $$(a^{2^0})^{k_0} = a$$, conversely b = 1 as you are raising to 0, and any number to the power of 0 is 1. Next step iterates over the remaining bits of k, that is $$k_1 \dots k_t$$. Basically this loop do the same process described above. Compute A squared (corresponding to this part of the expression $$(a^2$$), if the bit $$k_i = 1$$ multiply $$(a^{2^i})^{1}$$ by b, if $$k_i = 0$$ nothing is done, because the whole expression $$(a^{2^i})^{0} = 1$$. When the loops ends, b is returned.

In order to let you understand this process better, imagine you want to compute $$2^5\pmod 5$$. First step is representing the exponent in binary form, $$5 = 101_b$$, follow the steps, where a = 2, k = 5 and n = 5.

b = 1
A = 2
is k_0 == 1? yes -> b = 2
from k_1 to k_t:
A = A * A mod n -> 2 * 2 mod 5 = 4
is k_1 == 1? no
A = A * A mod n -> 4 * 4 mod 5 = 1
is k_2 == 1? yes -> b = A * b mod n -> b = 1 * 2 mod 5 = 2
return b, which is 2


I've tried to draw two graphics representations to help you visualize the process, lets say you want to compute $$2^7 \pmod 5$$ and $$2^{11} \pmod 5$$:

I hope this examples have let you understand better how the algorithm works. I've implemented it in python, source code is available on github:

def powerModInt(a,k,n):
"""
@input a in $Z_n$ and integers 0 <= k <= n
@output a to the power of k mod n ($a^k mod n$)
"""
b = 1
if k == 0:
return b
A = a
# If the least significant bit is 1, $a^1 = a$
if 1 & k:
b = a
k = k >> 1
while k:
A = (A**2) % n
if 1 & k:
b = (b * A) % n
k = k >> 1
return b

## Order

The definition of a number's order is $ord(a) = min(k\ \in \mathbb N\backslash 0\:a^k=1)$ that is to say, the smallest number to which you have to raise a to give you 1. For example, in $$\mathbb Z_5$$ you have the following orders for its elements:

• $$1^1 = 1; ord(1) = 1$$, The smallest number to which you have to raise 1 to give you 1 is 1.
• $$2^4 = 1; ord(2) = 4$$
• $$3^4 = 1; ord(3) = 4$$
• $$4^2 = 1; ord(4) = 2$$, The smallest number to which you have to raise 4 to give you 1 is 2.

You can read more on Order on Wikipedia Order (Group Theory) page.

## Subgroups and primitives

Given an element of $$\mathbb Z_p$$, for example, $$\lt a> = \{ a^k:\ k\in N \}$$ is a subgroup generated by a.

This is called Generating a set of a group.

For example, the subgroups of the units of $$\mathbb Z_5$$ are:

• $$\lt 1> = \{ 1 \}$$, because $$\forall k \in\mathbb Z, 1^k = 1$$
• $$\lt 2> = \{ 2^0 = 1, 2^1 = 2, 2^2 = 4, 2^3 = 3\} = \{ 1, 2, 3, 4 \}$$
• $$\lt 3> = \{ 3^0, 3^1, 3^2, 3^3\} = \{ 1, 2, 3, 4 \}$$
• $$\lt 4> = \{ 4^0, 4^1, 4^2, 4^3 \} = \{ 1, 4 \}$$

If you look closely, <2> and <3> generate $$\mathbb Z_5$$ completely, this elements are called primitives. Particuraly, <a> will be primitive if its order is maximum, in this case it is so, because $$\phi(5)=4, ord(2) = ord(3) = 4$$, which is the maximum. Furthermore, the order of a number sets the number of elements that generate the subgroup, as ord(2) = ord(3) = 4, this means this subgroups generate 4 elements, which are the number of units of $$\mathbb Z_5$$, they generate $$\mathbb Z_5$$ completely.

# References

The code shown along this series is hosted on my github

Cryptography course notes by Professor Jesús García Miranda, Higher Technical School of Information Technology and Telecommunications Engineering of the University of Granada.

Spot a typo?: Help me fix it by contacting me or commenting below!

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