Why ZKChaos choose ZK-Rollups？
Rollups are all the rage in the Ethereum community, and are poised to be the key scalability solution for Ethereum for the foreseeable future. But what exactly is this technology, what can you expect from it and how will you be able to use it? This time, ZKChaos will guide you to answer some of those key questions.
What is layer-1 and layer-2 scaling?
There are two ways to scale a blockchain ecosystem. First, you can make the blockchain itself have a higher transaction capacity. The main challenge with this technique is that blockchains with “bigger blocks” are inherently more difficult to verify and likely to become more centralized. To avoid such risks, developers can either increase the efficiency of client software or, more sustainably, use techniques such as sharding to allow the work of building and verifying the chain to be split up across many nodes; the effort known as “eth2” is currently building this upgrade to Ethereum.
Second, you can change the way that you use the blockchain. Instead of putting all activity on the blockchain directly, users perform the bulk of their activity off-chain in a “layer 2” protocol. There is a smart contract on-chain, which only has two tasks: processing deposits and withdrawals, and verifying proofs that everything happening off-chain is following the rules. There are multiple ways to do these proofs, but they all share the property that verifying the proofs on-chain is much cheaper than doing the original computation off-chain.
State channels vs plasma vs rollups
The three major types of layer-2 scaling are state channels, Plasma and rollups. They are three different paradigms, with different strengths and weaknesses, and at this point we are fairly confident that all layer-2 scaling falls into roughly these three categories (though naming controversies exist at the edges, eg. see “validium”).
How do channels work?
Imagine that Alice is offering an internet connection to Bob, in exchange for Bob paying her $0.001 per megabyte. Instead of making a transaction for each payment, Alice and Bob use the following layer-2 scheme.
First, Bob puts $1 (or some ETH or stablecoin equivalent) into a smart contract. To make his first payment to Alice, Bob signs a “ticket” (an off-chain message), that simply says “$0.001”, and sends it to Alice. To make his second payment, Bob would sign another ticket that says “$0.002”, and send it to Alice. And so on and so forth for as many payments as needed. When Alice and Bob are done transacting, Alice can publish the highest-value ticket to chain, wrapped in another signature from herself. The smart contract verifies Alice and Bob’s signatures, pays Alice the amount on Bob’s ticket and returns the rest to Bob. If Alice is unwilling to close the channel (due to malice or technical failure), Bob can initiate a withdrawal period (eg. 7 days); if Alice does not provide a ticket within that time, then Bob gets all his money back.
This technique is powerful: it can be adjusted to handle bidirectional payments, smart contract relationships (eg. Alice and Bob making a financial contract inside the channel), and composition (if Alice and Bob have an open channel and so do Bob and Charlie, Alice can trustlessly interact with Charlie). But there are limits to what channels can do. Channels cannot be used to send funds off-chain to people who are not yet participants. Channels cannot be used to represent objects that do not have a clear logical owner (eg. Uniswap). And channels, especially if used to do things more complex than simple recurring payments, require a large amount of capital to be locked up.
How does plasma work?
To deposit an asset, a user sends it to the smart contract managing the Plasma chain. The Plasma chain assigns that asset a new unique ID (eg. 537). Each Plasma chain has an operator (this could be a centralized actor, or a multisig, or something more complex like PoS or DPoS). Every interval (this could be 15 seconds, or an hour, or anything in between), the operator generates a “batch” consisting of all of the Plasma transactions they have received off-chain. They generate a Merkle tree, where at each index
in the tree, there is a transaction transferring asset ID
if such a transaction exists, and otherwise that leaf is zero. They publish the Merkle root of this tree to chain. They also send the Merkle branch of each index
to the current owner of that asset. To withdraw an asset, a user publishes the Merkle branch of the most recent transaction sending the asset to them. The contract starts a challenge period, during which anyone can try to use other Merkle branches to invalidate the exit by proving that either (i) the sender did not own the asset at the time they sent it, or (ii) they sent the asset to someone else at some later point in time. If no one proves that the exit is fraudulent for (eg.) 7 days, the user can withdraw the asset.
Plasma provides stronger properties than channels: you can send assets to participants who were never part of the system, and the capital requirements are much lower. But it comes at a cost: channels require no data whatsoever to go on chain during “normal operation”, but Plasma requires each chain to publish one hash at regular intervals. Additionally, Plasma transfers are not instant: you have to wait for the interval to end and for the block to be published.
Additionally, Plasma and channels share a key weakness in common: the game theory behind why they are secure relies on the idea that each object controlled by both systems has some logical “owner”. If that owner does not care about their asset, then an “invalid” outcome involving that asset may result. This is okay for many applications, but it is a deal breaker for many others (eg. Uniswap). Even systems where the state of an object can be changed without the owner’s consent (eg. account-based systems, where you can increase someone’s balance without their consent) do not work well with Plasma. This all means that a large amount of “application-specific reasoning” is required in any realistic plasma or channels deployment, and it is not possible to make a plasma or channel system that just simulates the full ethereum environment (or “the EVM”). To get around this problem, we get to… rollups.
Plasma and channels are “full” layer 2 schemes, in that they try to move both data and computation off-chain. However, fundamental game theory issues around data availability means that it is impossible to safely do this for all applications. Plasma and channels get around this by relying on an explicit notion of owners, but this prevents them from being fully general. Rollups, on the other hand, are a “hybrid” layer 2 scheme. Rollups move computation (and state storage) off-chain, but keep some data per transaction on-chain. To improve efficiency, they use a whole host of fancy compression tricks to replace data with computation wherever possible. The result is a system where scalability is still limited by the data bandwidth of the underlying blockchain, but at a very favorable ratio: whereas an Ethereum base-layer ERC20 token transfer costs ~45000 gas, an ERC20 token transfer in a rollup takes up 16 bytes of on-chain space and costs under 300 gas.
The fact that data is on-chain is key (note: putting data “on IPFS” does not work, because IPFS does not provide consensus on whether or not any given piece of data is available; the data must go on a blockchain). Putting data on-chain and having consensus on that fact allows anyone to locally process all the operations in the rollup if they wish to, allowing them to detect fraud, initiate withdrawals, or personally start producing transaction batches. The lack of data availability issues means that a malicious or offline operator can do even less harm (eg. they cannot cause a 1 week delay), opening up a much larger design space for who has the right to publish batches and making rollups vastly easier to reason about. And most importantly, the lack of data availability issues means that there is no longer any need to map assets to owners, leading to the key reason why the Ethereum community is so much more excited about rollups than previous forms of layer 2 scaling: rollups are fully general-purpose, and one can even run an EVM inside a rollup, allowing existing Ethereum applications to migrate to rollups with almost no need to write any new code.
OK, so how exactly does a rollup work? There is a smart contract on-chain which maintains a state root: the Merkle root of the state of the rollup (meaning, the account balances, contract code, etc, that are “inside” the rollup).
Anyone can publish a batch, a collection of transactions in a highly compressed form together with the previous state root and the new state root (the Merkle root after processing the transactions). The contract checks that the previous state root in the batch matches its current state root; if it does, it switches the state root to the new state root.
To support depositing and withdrawing, we add the ability to have transactions whose input or output is “outside” the rollup state. If a batch has inputs from the outside, the transaction submitting the batch needs to also transfer these assets to the rollup contract. If a batch has outputs to the outside, then upon processing the batch the smart contract initiates those withdrawals.
And that’s it! Except for one major detail: how to do know that the post-state roots in the batches are correct? If someone can submit a batch with any post-state root with no consequences, they could just transfer all the coins inside the rollup to themselves. This question is key because there are two very different families of solutions to the problem, and these two families of solutions lead to the two flavors of rollups.
Optimistic rollups vs ZK-Rollups
The two types of rollups are:
Optimistic rollups, which use fraud proofs: the rollup contract keeps track of its entire history of state roots and the hash of each batch. If anyone discovers that one batch had an incorrect post-state root, they can publish a proof to chain, proving that the batch was computed incorrectly. The contract verifies the proof, and reverts that batch and all batches after it.
ZK-Rollups, which use validity proofs: every batch includes a cryptographic proof called a ZK-SNARK (eg. using the PLONK protocol), which proves that the post-state root is the correct result of executing the batch. No matter how large the computation, the proof can be very quickly verified on-chain.
There are complex tradeoffs between the two flavors of rollups:
In general, Vitalik own view is that in the short term, optimistic rollups are likely to win out for general-purpose EVM computation and ZK-Rollups are likely to win out for simple payments, exchange and other application-specific use cases, but in the medium to long term ZK-Rollups will win out in all use cases as ZK-SNARK technology improves.
How much scaling do rollups give you?
A simple Ethereum transaction (to send ETH) takes ~110 bytes. An ETH transfer on a rollup, however, takes only ~12 bytes:
ZKChaos thereby picks ZK-Rollups as Ethereum scaling solution, which will perfectly adapt and adjust to its own gaming ecosystem.
ZKCHAOS is based on layer2 through ZK-Rollup technology, providing unlimited scalability and privacy. It provides a high-performance mixer for token holders on multi-chain, and transactions in ZKCHAOS do not require any gas fees.
In particular, ZKCHAOS is not a purely anonymous transaction protocol, but an interesting fair game platform. It means there is a large number of transaction volume contributed by platform players, not mixers.
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