Clockwork Nonfrontrunning
Speakers: Daniel Cline
Transcript By: Bryan Bishop
Category: Conference
ClockWork: An exchange protocol for proofs of non-front-running
https://twitter.com/kanzure/status/1231012112517844993
Introduction
Clockwork is an exchange protocol for proofs of non-frontrunning.
Exchange systems
Exchange systems let you trade one asset for another asset at a certain price. It’s simple and it’s supposed to be fair. In this work, we’re focused on centralized exchange systems. We’re not working with decentralized exchanges just yet. These don’t necessarily need to be cryptocurrency exchanges, either. Our work applies to both.
In exchange systems, users place orders which are requests to buy assets at a certain prce. Then the orders get matched by an order matching algorithm and then the order prices, and amounts, are displayed to others.
Frontrunning
The last talk was all about frontrunning. In the exchange world, it’s when the exchange makes decisions about what orders to match based on the order of orders they have already seen. They already see the orders and transactions in the orderbook. This allows the exchange in some cases to gain profit or advantage in a risk-free way. This is not a good thing. The profit or advantage gained by the exchange really belongs to the user. If it’s getting risk-free profit, it’s basically stealing from users since they would have gotten that profit in the first place.
I’ll show some examples.
Insertion
The first type is insertion, which is when an exchange inserts its own orders to try to gain an advantage or profit. So let’s imagine Alice places a sell order for an asset, and Bob places a buy order for the same asset. The exchange can see this discrepancy and put itself in the middle of those two trades. It will buy up Alice’s asset and then it will sell it back to Bob for an instant profit, and it can very easily see that this is worth doing since it doesn’t take any risk. It knows that it is going to be able to profit from this and it can just insert those two transactions and act like everything is normal. In this case, the exchange is stealing profit from Bob using insider knowledge and privileges.
Dropping
This is where the exchange will loook at what the incoming transactions are and selectively drop one of the transactions. The exchange places a transaction. The exchange will drop an order and get to sell at a higher price than it would have. Both of these examples motivate one of the security properties of our system, which is blind commitment. We don’t want the exchange to be able to see the contents of the orders before it can commit to matching them. We want the exchange to commit to matching the contents of those orders.
Blind commitment
There’s a few other properties we want. Blind commitment is the first property. Let’s look at a strawman design. We’re going to look at a commit-and-reveal scheme where we’re going to try to achieve this blind commitment property.
Have users submit cryptographic commitments to the orderbook. So a pedersen commitment, and then reveal them once the exchange says I will match whatever is behind this commitment. So there’s some users and an exchange. They will take some orders and commit to them, and then send them right to the exchange. There’s three commitments that the exchange has. The next thing the exchange will do is it will sign this set of orders. The exchange is going to take this set of orders and match them all at once. This is okay because the matching algorithm will decide which ones will get executed, which is no different from how exchanges work today or only a little bit different. Then it sends this signature and the set of orders to users. User can verify the signature thta it is from the exchange, that they are sending something valid, and they will be able to see if each of their commitments are in fact in that set. Finally, users will reveal their orders and the exchange will compute the results of the matching algorithm, conduct the trades, and everyone is happy.
While the exchange can’t insert orders after committing, it can do very bad other things as well. One option is that if not every order is revealed is that the exchange can execute the batch. Imagine if you have a set of– you have some commitments that are opened and some that are not, and you have to make a decision as to whether to execute the batch or wait for more users. From the outside, this looks no different from- either the exchange can not say that it received something, versus a user might be like I actually didn’t send it to the exchange. So the exchange can basically pretend it never received a user’s reveal for a commitment. The exchange can also insert a bunch of orders into this set of commitments and drop the ones that aren’t favorable once it sees everybody else’s orders, even if every single person actually revealed. So we have this issue of all these fake users, and the exchange can just say it didn’t receive some of the reveals for these fake users which is actually itself. Another option is that you can throw out the batch if you really don’t like what’s going on, you could just say nothing happened and we’re not going to run the protocol along unless we get everyone’s reveal. But in this case, a user can halt the system. So we have some user that doesn’t like the exchange or they are offline, and if they don’t reveal their value for their order, then the system just halts. This clearly doesn’t work for some pretty obvious reasons. Also, an exchange can just throw out a batch anyway.
Bonded commit-reveal
Instead of there being no cost to not revealing, you could just say okay I’m going to have users lock up their funds and then if they don’t reveal I’m going to take away those funds. This is going to go outside of our model; this could also be a solution for decentralized exchanges, but it presents problems for both.
Yes, it incents users to reveal rather than abort because they would lose their money if they don’t reveal. It disincentives malicious users. But one thing is that it ties up capital. So you don’t really get anything other than like knowing that the rest of the people might reveal their orders by tying up your capital. For users, it’s not super great especially if this cost is high. It might still be profitable to frontrun or profitable to not reveal if what you were going to reveal… and not revealing is more profitable than whatever you locked up. Someone with lots and lots of funds could still kill these batches.
Finally, exchanges or miners in the DEX case, could censor the reveals and not allow users to get their bonds if they didn’t reveal. There’s no way of telling if a user didn’t reveal or if the exchange is just censoring them.
Blinded execution
Because of all these issues, we need some more security goals for our system. The first one is blinded commitment. The next one is blinding execution, which means that everything that is valid in the batch will get executed. All the orders will get executed.
Next security goal is liveness which solves the issue of our malicious users. Invalid orders don’t prevent everybody else from being able to trade and don’t halt our system.
Can we prevent frontrunning?
We can probably not prevent frontrunning, but we are able to give them evidence that the exchange didn’t frontrun. The protocol consists of four main steps. The first one is setup where we have some parameters that we need to give to the users before we run the protocol. In the next step, we send timelock puzzles to the exchange. I’ll go into timelock puzzles very shortly. They send these puzzles to the exchange, and then the exchange commits to a set of these puzzles. Finally, the exchange will solve puzzles, and users will attest to non-frontrunning for the exchange. When users attest, before they do, the user is convinced that the exchange could not have possibly frontrun them with a high degree of certainty.
Key insight
Time commitments as invented by Dan Boneh in 2000 is a great idea for implementing an exchange protocol. We construct them a little bit differently. Time commitments and timelock puzzles makes sure that an exchange has no way of knowing orders before it commits to them, except by solving them. Because we don’t require a user to reveal if they don’t decide to reveal, we can guarantee that at some point all the orders will eventually get revealed.
Timed commitments
When you use timelock puzzles to implement timed commitments– so it’s not quite the same thing as time commitments in the paper but we have the same interface. We can timelock an order. We have a certain amount of time that we want this puzzle to take, and we input our message and the amount of time and it spits out a few things. It spits out a ciphertext, an RSA modulus and a prime p which is a factor of the modulus. It’s important to note that timelock puzzles take a certain amount of sequential time and they can’t be sped up with parallelism. You cannot speed up puzzles significantly using parallelism.
Using this prime p, you can get this trap door for this RSA modulus which we are assuming is the product of two primes. Using this trapdoor, you don’t have to do all the sequential work, you can just plug it in and it will output the result in exponentially less time than solving it otherwise. This is almost instantaneous.
You input the prime, you give the puzzles, and it outputs the message or the exchange order in our case. If there’s no trapdoor, it can still be solved in t sequential steps, and it can still be done with the modulus, ciphertext, and the amount of time. These are all things that everyone knows.
Timelock puzzles
We use timelock puzzles to do this. A timelock puzzle will take a message m and create a puzzle that can only be solved in t steps. The trapdoor lets us do less than t steps of course, if the user gives us that parameter. We also encrypt the order with AES-GCM and create a ciphertext.
System model
We have an exchange that wants to accept incoming orders and it settles everything based on a public matching algorithm. We have users that want to place orders on an exchange and we want to constrain the exchange from frontrunning.
The exhange we assume wants to match some orders. We assume there’s some people trading on the exchange and it wants to keep running this protocol and we assume it doesn’t want to just stop everything if it doesn’t get to frontrun. But we assume it might be malicious and it might want to try to frontrun. Users are the same way: they want to trade but they also want to gain an advantage over one another and they want to disrupt the trading process, so they are also potentially malicious. We assume the exchange has access to many parallel resources, and this should be fine since our puzzle is sequential. But it has lots of resources. Also, the exchange and users might be colluding with each other. Similar to how Walter could collude with the ethereum miners in the last presentation, we still want to provide users the guarantee that their order was not frontrun even if users and the exchange were colluding.
Setup phase
The exchange publishes a batch ID and a timelock puzzle parameter t which is how long it should take to compute. The batch ID is just for identification just so the exchange knows where to send orders to. It also publishes the matching algorithm that it is going to be used for the batch. When you finally get the results of the trading and see the execution, you can verify this. Without a public matching algorithm, you wouldn’t be able to verify the results.
Users will send over a security parameter which is the amount of time for a timelock parameter, that you cannot solve it less than this amount of time. This is integral to the security of our protocol that users know how fast timelock puzzles can be solved. If someone out there had a big ASIC farm for timelock puzzle ASICs and they could solve puzzles faster than anyone else in secret, then this wouldn’t be so secure. We’re just assuming that people generally don’t have that ASIC farm.
Protocol
The users then send the orders, they send the puzzles, they sign the data so that the exchange can verify it was them and the message wasn’t altered. They send the ciphertext, the modulus and the signature to the exchange. The exchange already has the time parameter since it sent it to the users, so the exchange can solve the puzzles if it wanted to. The users can be ready to send their order.
After the send phase, there’s the commit phase. It verifies all the signatures and makes sure none of the orders have been tampered with. It will take all the orders it will want to match and put them into the set and say okay I’m going to match these. It will assign a set of orders and send this back to the users so that the users can verify that the exchange did in fact send a set of orders. There’s no way to force (forge?) this for the exchange’s message.
Finally there is the attest step where users can figure out if they have been frontrun. They mark the difference in time from when they submit their order and when they finally receive this commitment to matching or set of orders from the exchange. If this difference in time is greater than the safety order they set before, then they don’t sign since they could have been frontrun. But if it’s okay, then they know that they could not have possibly solved the timelock puzzle and they could not have gained any information about my orders and therefore they could not frontrun and then the user signs. The user takes the previous signature from the exchange, they take the set of timelock puzzles, and they include their trapdoor. The reason why they include the trapdoor is that then if they send it to the exchange, if they do send it to the exchange then the exchange doesn’t have to do anywhere near as much work to solve the timelock puzzle. A user can choose to not sign, because maybe they think they were frontrun or something.
Finally, the exchange solves the timelock puzzles and sends the results to users. They put this into their magic parallel puzzle solver, and send the solutions to the users. It can’t solve any of these puzzles faster sequentially, but having the parallel resources can help for solving many puzzles simultaneously. Some of them don’t need to be solved because some of the users did in fact send their trapdoor value. They need to do basically zero work to solve those particular timelock puzzles.
Implementation
We implemented this. It’s implemented and benchmarked in go.
https://github.com/mit-dci/opencx
Future work
We want to see applications to this to smart contract based exchanges, and to see if timelock puzzles and verifiable delay functions could be applied to this area that would be really cool. Okay, that’s it.
Sponsorship: These transcripts are sponsored by Blockchain Commons.
Disclaimer: These are unpaid transcriptions, performed in real-time and in-person during the actual source presentation. Due to personal time constraints they are usually not reviewed against the source material once published. Errors are possible. If the original author/speaker or anyone else finds errors of substance, please email me at kanzure@gmail.com for corrections or contribute online via github/git. I sometimes add annotations to the transcription text. These will always be denoted by a standard editor’s note in parenthesis brackets ((like this)), or in a numbered footnote. I welcome feedback and discussion of these as well.
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