For the third installment of our “Open Source Monday” blog series, we provide a demonstration and example code of how Zero-Knowledge Proofs can be applied to clean energy applications.

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Zero-Knowledge Proofs (ZKPs) will be very familiar to blockchain experts and cryptocurrency enthusiasts. As we described last week, there are also non-financial applications for ZKPs in the clean energy sector. 

Imagine that you are an electric vehicle charging station company. You collect data from charging stations that have a huge amount of individual information. You collect and centralize that data for billing and maintenance. Some of the data could even be considered personally identifiable information. That is, time, location, vehicle type, etc. could be pieced together to identify an individual even without having a specific name of a person. 

In summary, your data is valuable. Others ask for it. The local utility wants access to help plan grid upgrades. The local government wants to use it to plan public transportation. Community groups want access to learn the impact on the environment. Do you hand over your data to them? A better approach would be to enable them to ask questions of your data in an anonymized way without you physically transferring the data. Fewer copies means less risk.

As an energy planner (e.g., utility, government, community group) how do I know the data is real if I never see it? This is where ZKPs come in. ZKPs can prove that the data is as expected without having to reveal the actual data itself. 

The video below shows an example of some of the steps to accomplish this. 

What you’ll see is:

1. Two different “customer” datasets are provided to form a single dataset. Continuing the analogy above, this is like the EV charging station company storing two different customers’ data in the company’s database.
2. That data is off-chain. A central Oracle creates a non-interactive proof.
3. The Oracle also converts the proof into an image.
   The data is an array and each value in the array
   is represented by a proof in the form of a grayscale pixel.
4. A new ZKP verification ERC721 compatible smart contract is created and an NFT of the image is minted.
5. Using Truffle, a smart contract can be called to execute the validation of data without revealing the identity of the individual.

For our implementation, we take advantage of some recent advances in ZKPs. In particular, many ZKPs rely on some interaction with a prover and a verifier. They ask each other a series of questions to validate the data. Here, we apply an approach inspired by zk-SNARKs. The “N” in SNARK is for Non-Interactive. The genius behind this approach is that all the proof can be provided in a single message to the verifier, eliminating the traditional back and forth. There is a LOT of math behind this which is out of scope for our example. You can learn more about the math and how it works thanks to Zcash. For simplicity in this example (see GitHub), we use a simple hash implementation rather than a full zk-SNARK.

One other addition that we made here is that since the proof is a simple message, we can codify it as an image. We can then mint the image as an NFT. The big advantage of this approach is that proofs are visible to everyone and searchable by anyone on a platform like OpenSea or Coinbase without revealing any individual data. This removes a transaction headache for the data owner. They don’t need a new system or special hosting, etc. to handle inquiries about their data. 

And, the last addition, is that we have an example Polygon smart contract that enables an individual to validate their own dataset. What the validation code enables is that the EV charging company and / or energy planners who incentivize data owners to validate data get greater assurance that the data is real. This is a great use case of data privacy enabled through crypto.

The basic principles here can be applied to many sectors where there is centralized off-chain data, strict data privacy needs, and a need to prove the data is valid. Next week, you’ll see an expansion of this proof to leverage more of the zk-SNARK stack.

This is the first installment of our blog series, “Mastery Monday with VIA” where we share with you some of the inside details of our technology and math in 5 minutes or less. So, are you ready to become a master?

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So, what is homomorphic encryption (HE)? 

HE is a way to compute something without knowing what the numbers are (i.e., keeping them encrypted). For example, adding two numbers or multiplying two numbers and learning the exact result while keeping each of the two input numbers secret.

Why does it matter? 

Unencrypted data is unsafe. With cybersecurity increasing, the last remaining case where data routinely remains unencrypted is during computations. Encryption is already commonly used when data is stored and when it’s sent from one place to another. 

When would you use it? 

Anytime you use math. Which is everywhere. A simple example is a census. I want to know how many people live in a town without knowing how many people live on any single street, in a particular building, or in their own home. AI is all math. So, voice and face recognition while the data is private is a more complicated example.

How does it work?

Check out this less than 4 minute video below! 

Please note: this video was filmed in VIA’s new office space, hence considerable echo. We recommend using the closed captions.

Want to learn more?

VIA holds several issued patents in homomorphic encryption. 

Other details related to VIA’s approach to HE are available from VIA’s earlier blog from 2020.

Next week, we’ll be sharing some code and a use for HE.

 

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Below is a transcript of the “Homomorphic Encryption Explained” video.

Everybody, everywhere is asking about homomorphic encryption.

Said no one ever.

It’s not actually a thing that anyone ever asks about, but it’s super important, nonetheless.

And the reason is because keeping our data safe is super important.

So, the idea that we keep data safe when it’s at rest – that happens. Where the data is not safe is when you actually try to do some math on numbers.

For example, you have some data or information [Becky] and you have some data or information [Sam] and you want to do a comparison.

So, both of you have kitten collectible porcelain statues. And now we want to know which one of you has more without either of you revealing your own number because your porcelain kitten collection data is intensely personal.

[Becky] How did you know? 

[Sam] True.

As a person who’s doing, you know, high academic research, I’m really interested in the total sum of kitty collectors in this world and how many porcelain kitties there are in the world. That’s my thing! I’m super interested in it.

Don’t judge me.

Okay, so here’s what we’re going to do. We’re going to now add up the number of porcelain kitties in your collection [Becky] and the number of porcelain kitties in your collection [Sam]. And Becky, you’re not going to tell Sam how many. And Sam, you’re not going to tell Becky. And neither of you are going to tell me. And, even so, as a world-class researcher, I am going to be able to tell how many there are in the world without knowing either of those things.

And that’s one definition of homomorphic encryption, which is the ability to add up or multiply or divide or subtract or be able do some math without ever actually revealing any individual number.

So, the way that we are going to do that is we’re going to pretend this is my homomorphic encryption specialist device.

It’s actually my calculator on my phone here.

And as an example, what I’m going to do first is type in a number that is only visible to me, the researcher. And to you, the audience, only. So just let’s keep it between us. 

And now, I’m going to pass this to Becky. Add to whatever number I sent you. I want you to type it in and add the number of porcelain kitties that are in your collection.

Now you’re going to pass that to Sam.

Sam, I want you to add how many are in your collection.

Ok, now you’re going to pass it back to me.

And just to be clear, Becky, do you know how many porcelain kitties Sam has?

[Becky] No idea.

[Sam] Do you know how many porcelain kitties Becky has? (shakes head)

And, I don’t know how many they have either individually but I do know that together we have 24 is my guess. But is that true? That’s what I have in my calculator. Let’s see.

Becky, how many were in your collection actually? 

[Becky] 8.

And how many were in your collection?

[Sam] 16.

So 8 and 16, I’m pretty sure is 24. So we got the number! Even though neither of you knew each other’s numbers and I didn’t know what your numbers were.

And, there’s a lot of things you can do together to continue to obfuscate and hide the original numbers.

And we’ll talk later about some use cases.

We have an upcoming piece just on a use case of homomorphic encryption and NFTs in gaming. So, we’ll look forward to sharing some code and why that may be useful next time.

But, here was a little bit of a theory of homomorphic encryption particularly applied to addition.

Thanks for watching!