Zero knowledge proofs (ZKP) have become one of the most talked-about privacy technologies in recent years, especially with the rise of blockchain and cryptocurrencies. However, most explanations on the internet dive deep into complex math without really explaining the basic concepts in an intuitive way.
My goal in this 2600+ word blog post is to demystify zero knowledge proofs by explaining what they are, how they work, their different applications, and limitations – all while avoiding jargon and math as much as possible. Think of this as your beginner‘s guide to understanding this revolutionary technology!
What are Zero Knowledge Proofs?
Let‘s break this term down:
- Proofs: Mathematical methods to prove the accuracy or truthfulness of a statement.
- Zero knowledge: Without revealing any information except the validity of the statement.
So a zero knowledge proof allows someone (the Prover) to prove to someone else (the Verifier) that a certain statement is true, without conveying any other information apart from the fact that the statement is indeed true.
For example, with a zero knowledge proof:
- You can prove to your friend that you have the password to a bank account without revealing the actual password.
- A website can confirm your identity without obtaining any personal information about you.
This is revolutionary because zero knowledge proofs provide a mathematical solution for transmitting secure information without risking data privacy.
A Simple Analogy: The Red and Green Balls
Let‘s understand this concept with a popular analogy featuring color-blind Charlie, who cannot distinguish between red and green colors.
- Charlie takes a green ball (G) in one hand and a red ball (R) in the other hand.
- You can clearly see which color ball is in which hand.
- Charlie then puts both his hands behind his back and either switches the balls between his hands or not.
- He then shows you his closed fists and you have to state whether he switched the balls or not.
Since you can distinguish between the green and red color, you can easily determine if Charlie exchanged the balls or not.
- If Charlie does this only once, you have a 50% probability of guessing correctly by chance whether he switched them.
- But if this experiment is repeated multiple times, the probability of you guessing correctly every single time just by chance becomes extremely low.
So even though Charlie cannot perceive the different colors himself, he can now be convinced that the balls are indeed different colors because you guessed correctly so many times.
This allows you to prove the balls are different colors without revealing what the actual colors are! Charlie has thus verified this statement through a zero knowledge proof!
Key Properties of Zero Knowledge Proofs
For a zero knowledge proof method to be valid, it needs to satisfy three key properties:
Completeness: If the statement made by the Prover is true, the Verifier can be convinced of this fact by the proof.
In our ball example, you were able to convince color-blind Charlie that the balls were different colors because they actually were different colors.
Soundness: If the statement made by the Prover is false, they cannot convince the Verifier that it is true.
If you tried the same ball experiment with two green balls for example, you wouldn‘t have been able to reliably state whether Charlie switched the balls or not.
Zero-Knowledge: The Verifier learns nothing beyond the validity of the statement.
Charlie did not find out what the actual colors of balls were – only that they were different.
Interactive vs Non-Interactive Zero Knowledge Proofs
There are two main types of zero knowledge proof systems:
Interactive Proofs: The Prover and Verifier exchange multiple rounds of information to prove the statement.
Our ball example with the back and forth switching of balls represents an interactive zero knowledge proof method.
Non-Interactive Proofs (NIZKs): The Prover generates a proof that the Verifier can check independently just once without any further interaction.
Think of non-interactive proofs as a sealed envelope. The Verifier receives the statement and proof in one go and can verify it by themselves without communicating further with the Prover.
So which one is better?
Interactive proofs aretypically more efficient from a computational standpoint. But non-interactive proofs are advantageous when interacting multiple times is difficult, expensive or time-consuming.
Many real-world systems incorporate both interactive and non-interactive proofs for optimal efficiency and convenience.
Real-World Applications of Zero Knowledge Proofs
While balls and colors make for simple examples to explain zero knowledge proofs, what are some real-world use cases leveraging ZKP technology?
1. Blockchain platforms
Public blockchains like Bitcoin and Ethereum harbor transparency problems because all transactions are openly verifiable. This leads to privacy issues and potential deanonymization attacks from analyzing transaction patterns.
Zero knowledge proofs can introduce privacy to blockchain transactions. The sender can prove they have enough funds to complete a transaction without revealing actual account balances or identities.
Some cryptocurrencies like Zcash and upcoming Ethereum upgrades rely extensively on zero knowledge proofs to facilitate anonymous transactions.
2. Authentication
Securely authenticating users is critical for many apps and services, but traditional login methods like usernames, passwords, or OTPs have security issues around identity theft and phishing attacks.
With zero knowledge proofs, users can mathematically prove their identity without ever providing private account information to the service itself. This eliminates exploitation risks.
Microsoft recently patented a zero knowledge based authentication system. Startups like Valid Network are pushing ZKP authentication to secure messaging, payments, and more.
3. Voting systems
Transparent voting systems enable public verifiability of votes but compromise voter anonymity. Zero knowledge proofs can enable voters to privately authenticate their eligibility and confirm their vote was counted without revealing who they voted for.
Systems like Microsoft ElectionGuard integrate ZKP cryptography to build transparent yet user-private voting systems. Spain‘s recent blockchain-based voting application also leveraged zero knowledge proofs.
Cryptography Behind Zero Knowledge Proofs
We‘ve explored several intuitive examples so far for explaining zero knowledge proofs. But in actual software implementations, they use formal mathematical proofs that likely go over the heads of average readers!
In this section, I‘ll attempt breaking down some core concepts without heavy math notation.
Hash functions
Hash functions are the building blocks of cryptographic proofs. A hash function takes an input string of any length and converts it into a fixed length alphanumeric string called a hash value.
- Small changes in the input lead to vastly different hash values.
- Hashes are one-way functions – you cannot retrieve the input from a hash.
- It is infeasible for two different inputs to produce the same hash value.
Hashes enable proving information without revealing the raw underlying data.
Commitment schemes
Commitment schemes allow the Prover tocommit to a chosen value by linking it to a random hash without revealing the selected value.
Think of this as hiding something inside a locked box. You cannot tell what is inside, but the box commits the Prover to whatever choice they made.
This hidden committed value can then later be revealed to verify whether the Prover was honest or lied in their original commitment.
Zero-knowledge succinct non-interactive arguments of knowledge
Try saying that five times faster! In short – zkSNARK (pronounced zk-Stark). This is a popular approach used to design zero knowledge proof systems.
Proofs designed using zkSNARK have some cool properties:
- Succinct – Small in size and can be quickly verified
- Non-interactive
- Does not reveal Prover‘s secret information
The most widely used implementation of zkSNARKs is Zcash – an anonymous blockchain platform for payments and transactions.
Ethereum is also transitioning to using zkSNARK technology for enabling scalability and privacy. Multiple startups are building custom zero knowledge solutions using zkSNARK proofs across domains like finance, communications, and more.
Hopefully this breakdown helps explain some of the terminology used in zero knowledge proof implementations! We barely scratched the surface but tried to cover enough to demystify the topic for most readers unfamiliar with cryptography.
Challenges Around Zero Knowledge Proofs
While zero knowledge proofs are extremely powerful as privacy tools, they also come with some unique challenges:
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There are no perfect guarantees. Verifiers can still be tricked by false proofs with very tiny probabilities – there‘s always asymptotic margin of error.
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Interactive schemes require multiple rounds of intensive cryptography to reduce probabilities of errors. This can get computationally expensive.
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Setting up the initial environment required for constructing proofs requires trusted setup between participants which comes with its own security assumptions.
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Average users may not fully grasp the guarantees or limitations around zero knowledge proofs leading to false sense of privacy.
Despite these limitations, zero knowledge proofs remain one of the most promising technologies for achieving secure computation and validations in a privacy-preserving manner.
Ongoing research around zkSNARKs and protocol improvements are helping tackle challenges around efficiency and security.
As zero knowledge cryptography and infrastructure matures, we are likely to see rapid advances and adoption across industries in coming years. Exciting times ahead!
The Future of Zero Knowledge Proofs
In 2022 alone, major IT companies like Google, Nvidia, Protocol Labs announced zero-knowledge focused research teams. Crypto payments company Circle transitioned USDC – world‘s second largest stablecoin – to public zero knowledge setup on Ethereum in August 2022.
ZK proofs are expected to drive disruption across sectors like:
- Decentralized Finance: Enabling private trading, transfers, transactions
- Crypto regulations: Better auditing and compliance around sealed digital assets
- Supply chains: Protecting business-sensitive logistics information while proving authenticity
- Healthcare: Shareable electronic health records without exposing raw sensitive patient data
Gartner lists zero trust capabilities including zero knowledge proofs amongst its top security and risk management trends for 2023.
Forbes technology council expects zero knowledge proofs to go mainstream in 2024.
The progress in zero knowledge cryptography has been stealthily fast. As per Consensys founder Joseph Lubin, ~$2B+ funds are invested into zero knowledge related startups and projects as of June 2022.
So watch out for major milestones around scalability, security and real-world deployment of ZK proofs in near future!
Final Thoughts
I hope this guide helped demystify zero knowledge proofs for you without getting too technical. We discussed:
✅ What zero knowledge proofs are
✅ Simple examples illustrating the concept
✅ Key properties around completeness, soundness and privacy
✅ Comparison between interactive and non-interactive proofs
✅ Real use cases like blockchain, authentication and voting
✅ Overview of cryptographic concepts behind ZK proofs
✅ Current challenges and future outlook
Zero knowledge proofs are poised to make computation more secure, transparent and private across industries. Understanding this revolutionary technology is valuable regardless of your field or domain expertise.
Have questions? Want to discuss more on ZK proofs? Feel free to leave a comment below!
