# Chainalysis: Building Trust in the Ethereum Blockchain by Mikkel Jensen & Surya Rastogi

- Channel: [Devcon](https://streameth.org/devcon)
- Date: 2020-10-09
- Duration: 15:54
- Topics: blockchain, smart contracts, distributed ledger, ethereum, Science & Technology
- Watch: https://streameth.org/watch/yt-lDWS_NLvwoA
- YouTube: https://www.youtube.com/watch?v=lDWS_NLvwoA

## Description

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https://archive.devcon.org/archive/watch/5/chainalysis-building-trust-in-the-ethereum-blockchain

Mikkel Jensen & Surya Rastogi give their talk on Chainalysis & Building Trust in the Ethereum Blockchain

Speaker(s): Mikkel Jensen, Surya Rastogi
Skill level: Intermediate
Track: Execution layer
Keywords: Chainalysis, trust, technical

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## Transcript

So, I'm not sure if you know, but we're from a company called Chainalysis. Today we will delve into building trust in the Ethereum blockchain and Ethereum blockchain and what our company is doing in this area. Sorry. I'll tell you more about it later. Okay, great. So, a quick agenda on what's going on. First, we'll talk about Chainalysis and our services. Then we'll delve into some of the data issues we face. Then we will have a long section where we will analyze some Ethereum data and look at one of the studies we conducted. And then, in general, let's talk about the conclusions we have drawn. So, a brief introduction to me. I am the lead engineer for the blockchain analytics team at Chainalysis. But before that, I was very interested in Bitcoin academically. So for my undergraduate thesis, I created a toolkit for visualizing the Bitcoin blockchain. This was for Imperial College and here are some images from my thesis. In the top left corner you can see here, um, different views of Bitcoin transactions. Here, this one has one input and two outputs. The old way of change he is considering. There are many inputs and one output, so someone is aggregating a lot of funds. But here, on the contrary, there is one input and many outputs, so someone is disaggregating the funds. Um, that has changed a lot since I finished this thesis around 2015. So, block size wasn't that big of an issue back then. Packaging wasn't something that was implemented or, um, used regularly. So blockchain itself has changed a lot since then . Um, a little about myself, I really like network theory and graph analysis network theory and graph analysis . Um, this fun little visualization in the corner visualization in the corner is, um, an algorithm that I modified, which some of you may know, called ForceAtlas2, and I created a multi-force, multi-gravity ForceAtlas2 to separate different, um, graphical entities. Um, and down here you can see the cluster over time. So, what does Chainalysis do and what services do we provide? Well, we're actually trying to build trust in the traditional ecosystem. So I know some people here might want a disparate financial ecosystem, but our job is to integrate crypto companies into the existing, um, ecosystem. So we're working with three main, um, parts of this ecosystem. We work with cryptocurrency companies themselves, financial institutions, as well as law enforcement agencies and regulators. So we found that overall the space is not evolving because neither side feels comfortable with what is being proposed. So you can see the over-regulation over-regulation that has taken place in Japan, as well as Korea, coupled with some exchange hacks that have resulted in many private coins actually being removed from the exchange. So, first we see that Coincheck had to remove several of its confidential coins from the exchange confidential coins from the exchange . Then it spread to South Korea and they started implementing stricter regulations and more exchanges followed suit. So our goal is to provide tools that allow regulators to feel comfortable with these currencies and what they can do with them to some extent. Thus, overall illegal activity hinders the growth of all ecosystem participants ecosystem participants . I'm sure some of you have mixed opinions about the top half, but I'm sure we can all agree that the bottom two are bad. In general, each of these ecosystem participants has their own risks and needs, and we try to take each of them into account to take each of them into account . So, it is difficult for cryptocurrency-based companies to obtain bank accounts. It is difficult to establish a banking relationship banking relationship without having some information about where your funds are coming from and what risk is associated with them . For financial institutions, if they expose themselves to too much risk, they damage their reputation, and if they are large financial institutions, they will face legal consequences. And finally consequences. And finally , for law enforcement , if they can't enforce the laws they pass or the regulations they make to control the industry, they won't be happy with the growth of this technology. So overall, our goal is overall, our goal is to work with every participant in this ecosystem and provide them with value that will allow them to feel more comfortable with cryptocurrencies in the existing financial system. So, some of the data challenges we face. We used to be a very Bitcoin-focused company. Hmm, but now we're paying a lot of attention to Ethereum. So Ethereum. So , here is actually Bitcoin on the left and Ethereum on the right. They may look similar because they are graphs, but they are actually quite different in terms of their basic structure. So, on the left is the UTXO structure, um, with all the UTXO coins, and on the right is the most account-centric structure that we see in any account-based model. In addition, smart contracts provide a new necessary level of analysis. We need to go beyond transactions and delve into the execution traces themselves. Um, we need to see what type of execution is happening and what information we can extract from it. Um, so this is an example of a data field in Ethereum, um, and this is pretty old-fashioned because there are much more advanced decompilers, but you're decompilers, but you're essentially decompiling the data field to try to figure out what parameters were sent. Um, moving on to risk analysis. So, the tools we provide to ecosystem participants are, in essence, risk analysis. And they vary depending on each client. So if you are, say, in a country that does not consider gambling to be high-risk, you will have individual notifications that do not inform you that gambling is high-risk for you. But, if you are, for example, in the USA, it will be high-risk. That's why we provide cryptocurrency companies with a customizable tool known as KYT so they can choose the right risk alerts. Good. I will now hand it over to Mikko, who will walk you through one of our Ethereum data research and analysis. Yes, my name is Mikkel, and I work in our research department at Chainalysis. So I actually have the opportunity to do some interesting things in the company. So I'll talk a little bit about some of the interesting things we do. Um, and the subtitle is what happened to Ethereum volume in 2017. So, that's roughly the story I'm going to tell you today. And then I asked my mom to send me a photo for the presentation. So, this is me at the beginning of my career in data science. I actually think I'm playing Counter-Strike on this...But just so you know, we don't take ourselves too seriously. So if you find any of this interesting, please come talk to us later. We're pretty nice guys. Um, so basically we're starting with the idea of ​​knowing nothing about blockchain. So, this is one visualization of addresses interacting in some blocks. And then our goal is then our goal is to turn that into this. So, we have an idea of ​​what belongs to someone, how different entities interact, so we gain knowledge about who interacts with whom in the blockchain. So, this is a kind of background information. It's like we're monitoring the blockchain and trying to understand what's going on, and if there 's something we don't understand that piques our interest and we think, "Wow, what's going on here?" And then this graph shows the cumulative volume moved by the Ethereum network. And oh, I can point. Steeply. So, initially there was a completely normal growth during this period, and then starting from this period, it's almost like a piecemeal function. So you have something like a normal growth in volume moving into Ethereum, and then it turns into a fivefold growth. So it's quite interesting to watch. It's like to watch. It's like , well, we know a lot happened in 2017, ICO-wise, there was a bull run in cryptocurrency, but that doesn't really explain why you were moving so much ether around the network. So, a starting point is that we see something here or we can't explain it, so let's figure out what's going on. So we can if we divide these periods into two blocks, so we look at the initial period in the network and then this period of high growth. One of the things we can do is look at the frequency distribution. This means that we take all the transfers from this initial blue period and say, " period and say, " Okay, what volume was sent in this period and what amount accounts for the total volume?" So, this graph shows that a higher total volume was sent by the network due to smaller transfer amounts. This makes sense if you consider the overall economic network, where most people don't have a lot of money, so they're likely to interact with smaller amounts. So, a larger volume will be moved through smaller amounts. So, at least for me, it's intuitive when I look at it as normal behavior. So what happens when we impose the 2017 period? This is quite interesting. So to me, this definitely doesn't look like normal economic behavior. It looks almost artificial because you have these almost smoothed areas and then they just kind of fall off. So when we see this, one thing is quite noticeable: more volume is moving through larger amounts on the network. So, again, one can hypothesize: why do some people or organizations move more funds due to larger amounts? Then you can, I mean, we're essentially detectives, so having a list, well, is that because someone wants to artificially increase volume, or is it just more people transacting in the normal way? This definitely doesn't seem normal to me. So we can also look at what types of addresses were involved in this increase in network traffic. And so, this graph essentially shows what, for example, how much volume was sent through old addresses during this period. And what I mean by old addresses is addresses that have been seen before in the blockchain, um, relatively. So, if we're in this column, for example, then old addresses mean addresses that have been seen before. Or, if we're here, it means all the volume that has been moved to the addresses that have been seen up to this point. So, this essentially shows how much volume is moving to addresses that have been seen previously in the blockchain. And then we can also overlay a graph of how much volume is moving to addresses that were only seen or first seen in that block range. So, essentially, newly generated addresses for any point on the timeline. So when we do that, it also looks cool because here we definitely have a clear pattern that something is happening in 2017. For example, there's a lot of volume being moved , and we know it's in large volumes, and now we also know that it's, um, about newly generated addresses. So, I mean, I could show you 100 slides, and that's just a very small idea of ​​how we would analyze something. So, we obviously have a lot more information to say what's going on in the blockchain, but when we put it all together, then we can, um, have an idea of what the addresses are. So, we know they are newly generated, we know they move a lot of funds in large volumes, and we also know a bunch of other things. So, we can determine which addresses are participating in this movement. And then we cluster them together. And when we're done with that, we can also do some graph analysis. So graph analysis. So , we look at how they interact? Do they interact the same way? Because even if you have a specific address, you can still interact with other addresses differently. So this doesn't mean you should be clustered together clustered together . So it's a long process, but when we finish, we basically end up with something like this, where it's a visualization of 25 blocks in 2017. And all the orange dots here are addresses that were linked together, which is essentially responsible for this huge movement of funds. Um, I just like graphs, so I just wanted to show you a closer look. Um, what can we say about... can we say about... Yeah, another thing is that we know that the addresses responsible for this belong to each other. So, it is one entity. What can we say about this entity, knowing entity, knowing that it was responsible for moving such a large amount of ETH in 2017? Well, we can say, " can say, " Okay, we know that all these addresses belong to each other." Let's see how much volume they send to each other." And here it is expressed as a percentage of the total network volume. So, in these blocks, about 60-70% on average was sent inside this entity. And then the orange color is, in fact, the means that go beyond this essence beyond this essence . So, that's right, about 2-3% that leaks out over time. So, there is a huge difference between how much volume is moved and how much volume is sent outside the entity. So, yes, this is...And I would like to take a closer look at what this entity looks like. This is a project that lasted several months. So, these are just high-level details. But it's really interesting when you find something that accounts for... accounts for... For example, for some periods it's over 90%. So over 90% of the volume actually comes from a single organization in 2017 on the network. Yes. Returning to Syria. Good. Um, well, we're a very growing company, and we're expanding quite quickly. So specifically on my team, if you want to work with Mika and me, we're looking for a data scientist and a product manager. So, if any of this interests you, be sure to reach out and chat with us. Um, besides, the company generally hires employees in many areas. Um, and we also have large offices around the world offices around the world . And in New York, Washington, Copenhagen, London, um, we have a lot of smaller support offices, I think they will appear all over the world. So wherever you are, you can probably work for us there work for us there . Hmm, we're also very convenient for remote work, so even if you don't want to work in an office and work remotely from remotely from anywhere, hmm, we're also very convenient for remote work. In particular, my team, half of us are spread across Europe, and some of us are Europe, and some of us are in New York. In addition in New York. In addition , experience with cryptocurrency is not required. So if you want to learn about the industry, if you want to get useful information, we will be happy to teach you and help you with that, and you will get it in practice. And this is not a prerequisite. So yes, if this talk was interesting to you, please come talk to us in the sponsors' gallery. Hmm, and yes, come say hello. Hmm.