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When people talk about zero-knowledge encryption in 2024, they often refer to use cases focused on privacy that relies on a combination of blockchain technology, cryptocurrency, digital wallets, and users with some degree of Web3 knowledge.
Zero knowledge proof has been around since the 1980s, long before the advent of Web3. So why limit their potential to blockchain applications? Traditional businesses can adopt ZK technology without fully adopting Web3 infrastructure.
At the basic level, ZKPS unlocks that the ability to prove something is true without revealing the underlying data behind that statement. Ideally, Prover creates the proof, verifiers verify it, and these two actors are completely isolated from each other to ensure fairness. That’s really it. There is no reason why this concept must be trapped behind the learning curve of Web3.
Most organizations that may benefit from ZK technology are not using blockchain or are not aware of Web3. The industry is still young, and many are now familiar with Bitcoin (BTC) and Ethereum (ETH), not to mention Layer 2 and 3S.
Nonetheless, ZKP is already applicable to a variety of real-world use cases and does not require complete Web3 rail integration.
Do you trust slot machine payments?
With zero knowledge proofs, there is no need to trust game operators. Knowing that the game is designed fairly, you can enjoy and reassure yourself of playing. All digital gambling machines in the world need to be designed with ZKP. It makes sense for the operators and players. The best part is that players can benefit from the word “web3” or “crypto” without the word “introducing” in their mind.
Recently, DraftKings and White Hat Gaming were fined $22,500 for online slot machine games by Connecticut. This failed to pay the winner for a week in August 2023. The game touted that nearly 95 cents will be paid for every dollar bet, so the algorithm should return $19,570 to players who bet on a $20,600 spin. Instead, the player lost $20,600. All of that went to Draft King.
This is where proof of zero knowledge can make a huge difference. ZKP can prove that the game has paid a certain amount for a certain period and a certain hit rate without revealing the individual spins or player identity.
This is great, but there are still issues with validating the proof. Someone needs to make sure that draft king or properly builds the proof based on all the required data. It may be a draft king in itself, but we should not trust them to handle their own validation. The regulator or auditor can do that, but this will likely cost a lot of money on drafting, which will be handed over to the client.
In this situation, the best option is public and distributed networks specially built to validate certifications in a quick and cost-effective way. Instead of being asked to trust a centralized entity, we can trust a decentralized protocol that ensures that fraudulent actors (i.e. those who try to verify false proofs) will be punished for committing fraud.
AI output and reliability
The possibility of AI deception is established. However, there are ways to leverage AI’s creativity while trusting its output. As artificial intelligence permeates every aspect of our lives, it becomes increasingly important to know that the models that train the AI ​​we rely on are legal. Using ZKML, or zero-knowledge machine learning, avoids these potential pitfalls.
Recently, the University of Southern California partnered with the Shoah Foundation to create something called Iwitness. There, users can speak or enter directly into the hologram of Holocaust survivors.
This is definitely a powerful use of machine learning. There’s something that’s very strangely touching to interact with the holograms of Holocaust survivors and feel like you’re having a real conversation. However, because the subject is sensitive to this, it is even more important that the algorithm underlying the hologram generates factual information.
Enter the zero knowledge proof. If you reconsider this project, you might consider adding “proof of algorithm output” which allows you to see evidence that the answers you are looking at are based on a majority of historical transcriptions and correctly trained natural language processing algorithms in interviews with Holocaust survivors.
ZKP allows us to obtain this input data and evidence of AI training without revealing the underlying information. To fact-check Holocaust information, you may need to read through a vast amount of data and end users may need to download or access a large dataset before reading or watching interviews. ZKP allows users to abandon this boring, resource-intensive process.
In this case, you might trust USC to validate the proofs for this particular project, but there are certainly many use cases in AI where end users may not want to trust centralized entities to create and validate proofs. When the incentives to construct “fake” proofs and validate them are adjusted, distributed proof verification makes the most sense.
ZK is a decentralized system that is unreliable for everyone
We have ZK so we don’t need to trust businesses or robots to tell us the truth. Many industries can level up with zero knowledge blockchain solutions, even if they know nothing about Web3 space.
By leveraging ZK’s proof verification, businesses and institutions can continue doing everything that was essentially an infrastructure. They need to create a simple system for proof creation and handle the verification of the proof using distributed systems like Zkverify. Even if a blockchain is used, users don’t need to worry about it.
ZK’s future is large and organizations don’t need to change much to benefit. They can just plug and play.
