The Power of Zero Knowledge Proof

I am an avid fan of 'Zero Knowledge Proof'

The core idea of Zero Knowledge Proof- is to demonstrate that you know/own a thing without actually revealing the thing.

In other words, it means one party (the prover) can convince another party (the verifier) that a statement is valid without sharing the actual information behind that statement​ . The verifier learns nothing beyond “the statement is true.”

In practice, ZKP(Zero Knowledge Proof) often involve clever math where the prover and verifier go back and forth to confirm truth.
But, the key here is no sensitive data gets exposed.

This is very handy, when designing privacy preserving software systems.

Today, we will not be going through the clever math, but rather I will try to show some real world examples of Zero Knowledge Proof, in software design. And how understanding the essence of it, can help us design and build more secure systems.

First, two quick examples of Zero Knowledge Proof, that are highly relevant to software development :

Example 1: Age Verification without Revealing Date of Birth
Your app needs to verify that a user is of 18+ age. Usually this verification involves, the user uploading their government issued ID card in the system.

But, notice that we only need to know if the user is 18+. But, required users to submit the entire ID card, which shared a lot of sensitive data in our system like full name, ID number, and date of birth.

With zero knowledge proof , this process becomes privacy-preserving:

A local ZKP app (completely offline) on the user's device processes the ID and verifies if the date of birth is before a cutoff date (18 years ago).

It then generates a cryptographic proof of this fact—without exposing the actual date.

Then only the cryptographic proof is shared with the verifying app , not the entire ID card.
The verifying app, can easily verify the proof, without ever knowing the user’s date of birth or full ID.

Example 2: Verifying Server Identity Without Revealing the Private Key
When you visit a website, your browser checks if it uses HTTPS. This relies on SSL/TLS certificates.

In a typical handshake, the website sends:

-Its SSL certificate

-A piece of data signed using its private key

Your browser then:

-Verifies the certificate is valid

-Checks that the signature matches the website’s public key

This proves the website owns the private key—without ever revealing the key itself.

Understand that in both these examples, the key idea is demonstrating that you know/own a thing without actually revealing the thing.

Okay. So, why should a developer care about this?
Understanding zero knowledge, and borrowing this idea in designing software systems helps in 3 ways

Privacy by design
Firstly, it protects sensitive information by design. It allow systems to verify truths without exposing secrets, massively reducing the risk of leaks, hacks, and insider threats.

ZKPs don’t just hide data—they make it unnecessary to share it in the first place. This shift reduces compliance burden, improves security posture, and supports a more ethical data handling model.

Automates Digital Trust
secondly, it enables digital trust without trusting the other party. So it scales digital trust.
Traditionally you needed trust a third party (bank, government, platform etc ) to keep your data safe, tell the truth about claims, enforce rules fairly. You also relied on legal contracts.
Instead of relying on third parties or contracts, you can use math to prove the truth. This can radically reduce overhead, and automate trust.

Proof Of Computation
Thirdly, it enables to verify the result of any computation without redoing it yourself.
This is a powerful but little known use of 'Zero Knowledge Proof' .

The verifier doesn’t need to run the computation. They can verify the tiny cryptographic proof, and be almost mathematically certain(with very high probability) the computation was done right

You can run an arbitrarily large, arbitrary long program, and whatever the program outputs, you can make a tiny proof-signature that says "this is the output you'll get if you run this program yourself".

The proof-signatures are relatively small, and you can verify them on small devices in milliseconds.

Another computer can trust the claimed output without having to run the program itself, by verifying the proof-signature.

This scales to arbitrarily large computations, so for example if a supercomputer says "I ran a quadrillion petaflops of your program (e.g. protein folding, scientific model) for 1 year, and the result was the picture attached to this signature", you actually can verify that the picture is correct, quickly and efficiently - without having to trust the supplier.

It's as good as if you re-ran the program yourself (up to cryptography-grade probabilities, which is good enough).

That’s all for this week folks, see you next week