Prime numbers are the building blocks of arithmetics. In this short post we will investigate some attributes of prime numbers and how to work with them in a computer. All the algorithms here can be found in a python notebook.

One of the main applications of prime numbers is in some algorithms related to cryptography (e.g. RSA) and therefore it is related to techniques developed for privacy preserving machine learning. In a previous post we have defined groups and fields using prime numbers and our main aim in this post is to show how to calculate prime numbers and check whether a certain number is prime or not. Due to the mathematical importance of primes I considered worthwhile to write a full post about them.

A prime number is a natural number that is only divisible by himself and 1. For instance 7 is a prime number because there’s no natural number smaller than 7 that divided by 7 results in an integer. Some sorted prime numbers are {2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, …}.

The building blocks of arithmetics

The fundamental theorem of arithmetics (a.k.a unique factorisation theorem) states that every integer greater than 1 either is a prime number itself or can be represented as the product of prime numbers and furthermore this representation is unique. So given an arbitrary integer a we can write it as

unique factorisation of a composite number

where the p’s are prime numbers and e’s are the exponentiation of those.

But let’s get back to topic, we are interested in cryptography, what does all this have to do with it?. Well, in modern cryptography one tries to find hard mathematical problems to solve that are easy to check. Factorisation of prime numbers is a very difficult task to solve. There are algorithms like Pollard’s factorisation or Lenstra elliptic-curve factorisation that are efficient but if you have a quantum computer at hand the best by far is the Shor algorithm (it is polynomial in log(N)). In RSA algorithm one chooses two large prime numbers p and q and compute N=p·q, the task of the adversary to break the code is to find the factors of N. For sufficiently large prime numbers the probability of solving this task by chance is negligible and the classic factorisation methods take too long so in practice it is impossible to crack in today’s computing power based on binary operations. Conversely having p and N it is very easy to check if p is a factor of N with one operation. This is also a requirement to modern crypto protocols.

How many prime numbers are there? Can we find a “magic formula” to get them all?

It would be nice to have a formula that when you input “give me the 532 prime” it would output 3833 (the 532th prime), all that taking O(1) compute time. Unfortunately this formula does not exist and finding new prime numbers take a lot of computational effort. That means that they are not predictable and this is one of the mysteries on primes… once you see a prime you don’t know when you’ll find the next one. Is there some kind of random/chaos in prime number structure? Nobody knows yet.

Even though we can’t predict primes with a formula we can calculate the probability of finding a prime number in between a range of numbers. We define Pi(x) as the number of prime numbers smaller than x. For instance, Pi(20)=8, because prime numbers smaller than 20 are {2, 3, 5, 7, 11, 13, 17, 19}. Therefore, to calculate the number of primes between x2 and x1 (x2>x1) we just need to subtract both Pi(x2)-Pi(x1). Here Pi(x) is exact calculation and we have to do it numerically. The prime number theorem establishes the asymptotic distribution of prime numbers by approximating the count to x/ln(x)

A graphical representation of this result is shown below:

Ratio of prime number counting function to two approximations. From Wikipedia

In the graph you can see how the count of prime numbers (the exact one) divided by the approximation tends to 1 for large x for the two approximations, the quotient and the integral. Actually the best approximation is the integral one. This result is very interesting and took many years to find, it gives some structure to this madness of prime numbers. But still, we can’t predict them and this is good for crypto!.

Primality testing and prime generation

A naive way to generate primes

The sieve of Eratosthenes

Primality testing and Miller-Rabin algorithm

So given a natural number n, how can you tell if n is prime or not? Remember first Fermat’s little theorem: Let p be a prime number and let a be any integer then

We may use it to check if n is prime. If we plug n instead of p to the above equation (taking for instance. a=2) and find that a^{n-1}(mod n) is 1 then can we say that n is prime?. The answer is no, because Fermat’s little theorem just goes in one direction (we need to know for sure that p is prime). It does however give a good indicative that maybe n is prime. So what if we test many different a? If we find that the power is 1 for all of them can we say that n is prime? Unfortunately the answer is again no. There are in fact numbers known as Carmichael numbers that are composite and its powers a^(n-1) are always 1. A well known Carmichael number is 561=3·11·17 and whose powers are

powering the Carmichael number 561

for all a smaller than 561.

Seems we are in a cul-de-sac situation… But hopefully we have the Miller-Rabin algorithm to help us. This test was developed by Miller in 1976 as full deterministic test but then modified by Rabin in 1980 to make it a probabilistic algorithm. In order to circumvent the Carmichael numbers let me make the following proposition: Let p be a prime (different from 2) then we can write p in the form of

where q is an odd number and k an integer. Now let a be any number not divisible by p. Then one of the following two conditions is true


for c in {1, 2, 4, …, 2^k}. You can find the proof of this proposition is the book of Hoffstein, Pipher and Silverman. This happens strictly when p is prime but let’s substitute this p for an arbitrary number n. We can write n-1=2^k*q and find a such that both conditions above are not fulfilled, then we call a a Miller-Rabin witness for the compositeness of n. I.e. if we find such a we know for sure that n is composite.

Ok, but how many random a’s should one test to give a certainty of say 99% that the number p is probably prime?. There’s another proposition that assesses that. Let n be an odd composite number, then at least 75% of the numbers between 1 and n-1 are Miller-Rabin witnesses for n. This means that if we randomly sample 10 distinct values of a in the range of 1 to n-1, the probability of hitting at least 1 witness is 1- P_Bernoulli(k=1, n=10)=0.99997. We are therefore quite sure that if with 10 trials we haven’t found a witness the number is prime but we will never be 100% sure.

My implementation in code of the Miller-Rabin algorithm can be found here. Also the code to generate random prime numbers. Checking the calculation time with a regular laptop (2.6GHz 6 core 16 GB RAM) I get in between 25ms to 60ms to generate a 256 bit random prime, in between 80 and 120ms for 512 bit prime and between 500ms to 2.15s for 1024 bit prime. All this checking for 40 maximum values of potential witnesses a.


Thank you for reading!. If you like the article, please star my github repo and give me some claps in the article.

PhD in Physics, AI Research Engineer

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