Ethereum white paper buterin
Read 2 reviews from the world's largest community for readers. Satoshi Nakamoto's development of Bitcoin in has often been hailed as a. Ethereum implements this paradigm in a generalised manner. tween diverged blockchains, EIP by Buterin [b] wiki/White-Paper. Vitalik Buterin. Ethereum white paper predicted DeFi but missed NFTs: Vitalik Buterin. Buterin still believes that “the internet of money should not cost. WAVES CRYPTO PREDICTION
In general, code execution is an infinite loop that consists of repeatedly carrying out the operation at the current program counter which begins at zero and then incrementing the program counter by one, until the end of the code is reached or an error or STOP or RETURN instruction is detected. Unlike stack and memory, which reset after computation ends, storage persists for the long term. The code can also access the value, sender and data of the incoming message, as well as block header data, and the code can also return a byte array of data as an output.
The formal execution model of EVM code is surprisingly simple. For example, ADD pops two items off the stack and pushes their sum, reduces gas by 1 and increments pc by 1, and SSTORE pushes the top two items off the stack and inserts the second item into the contract's storage at the index specified by the first item.
Although there are many ways to optimize Ethereum virtual machine execution via just-in-time compilation, a basic implementation of Ethereum can be done in a few hundred lines of code. Blockchain and Mining The Ethereum blockchain is in many ways similar to the Bitcoin blockchain, although it does have some differences. The main difference between Ethereum and Bitcoin with regard to the blockchain architecture is that, unlike Bitcoin, Ethereum blocks contain a copy of both the transaction list and the most recent state.
Aside from that, two other values, the block number and the difficulty, are also stored in the block. The basic block validation algorithm in Ethereum is as follows: Check if the previous block referenced exists and is valid.
Check that the timestamp of the block is greater than that of the referenced previous block and less than 15 minutes into the future Check that the block number, difficulty, transaction root, uncle root and gas limit various low-level Ethereum-specific concepts are valid.
Check that the proof-of-work on the block is valid. Let TX be the block's transaction list, with n transactions. If it is, the block is valid; otherwise, it is not valid. The approach may seem highly inefficient at first glance, because it needs to store the entire state with each block, but in reality efficiency should be comparable to that of Bitcoin. The reason is that the state is stored in the tree structure, and after every block only a small part of the tree needs to be changed.
Thus, in general, between two adjacent blocks the vast majority of the tree should be the same, and therefore the data can be stored once and referenced twice using pointers ie. A special kind of tree known as a "Patricia tree" is used to accomplish this, including a modification to the Merkle tree concept that allows for nodes to be inserted and deleted, and not just changed, efficiently.
Additionally, because all of the state information is part of the last block, there is no need to store the entire blockchain history - a strategy which, if it could be applied to Bitcoin, can be calculated to provide x savings in space. A commonly asked question is "where" contract code is executed, in terms of physical hardware. This has a simple answer: the process of executing contract code is part of the definition of the state transition function, which is part of the block validation algorithm, so if a transaction is added into block B the code execution spawned by that transaction will be executed by all nodes, now and in the future, that download and validate block B.
Applications In general, there are three types of applications on top of Ethereum. The first category is financial applications, providing users with more powerful ways of managing and entering into contracts using their money. This includes sub-currencies, financial derivatives, hedging contracts, savings wallets, wills, and ultimately even some classes of full-scale employment contracts.
The second category is semi-financial applications, where money is involved but there is also a heavy non-monetary side to what is being done; a perfect example is self-enforcing bounties for solutions to computational problems. Finally, there are applications such as online voting and decentralized governance that are not financial at all.
Token Systems On-blockchain token systems have many applications ranging from sub-currencies representing assets such as USD or gold to company stocks, individual tokens representing smart property, secure unforgeable coupons, and even token systems with no ties to conventional value at all, used as point systems for incentivization.
Token systems are surprisingly easy to implement in Ethereum. The key point to understand is that all a currency, or token system, fundamentally is, is a database with one operation: subtract X units from A and give X units to B, with the proviso that i A had at least X units before the transaction and 2 the transaction is approved by A. All that it takes to implement a token system is to implement this logic into a contract. The basic code for implementing a token system in Serpent looks as follows: def send to, value : if self.
A few extra lines of code need to be added to provide for the initial step of distributing the currency units in the first place and a few other edge cases, and ideally a function would be added to let other contracts query for the balance of an address. But that's all there is to it. Theoretically, Ethereum-based token systems acting as sub-currencies can potentially include another important feature that on-chain Bitcoin-based meta-currencies lack: the ability to pay transaction fees directly in that currency.
The way this would be implemented is that the contract would maintain an ether balance with which it would refund ether used to pay fees to the sender, and it would refill this balance by collecting the internal currency units that it takes in fees and reselling them in a constant running auction. Users would thus need to "activate" their accounts with ether, but once the ether is there it would be reusable because the contract would refund it each time. Financial derivatives and Stable-Value Currencies Financial derivatives are the most common application of a "smart contract", and one of the simplest to implement in code.
The simplest way to do this is through a "data feed" contract maintained by a specific party eg. NASDAQ designed so that that party has the ability to update the contract as needed, and providing an interface that allows other contracts to send a message to that contract and get back a response that provides the price.
Given that critical ingredient, the hedging contract would look as follows: Wait for party A to input ether. Wait for party B to input ether. Such a contract would have significant potential in crypto-commerce. Up until now, the most commonly proposed solution has been issuer-backed assets; the idea is that an issuer creates a sub-currency in which they have the right to issue and revoke units, and provide one unit of the currency to anyone who provides them offline with one unit of a specified underlying asset eg.
The issuer then promises to provide one unit of the underlying asset to anyone who sends back one unit of the crypto-asset. This mechanism allows any non-cryptographic asset to be "uplifted" into a cryptographic asset, provided that the issuer can be trusted. In practice, however, issuers are not always trustworthy, and in some cases the banking infrastructure is too weak, or too hostile, for such services to exist.
Financial derivatives provide an alternative. Here, instead of a single issuer providing the funds to back up an asset, a decentralized market of speculators, betting that the price of a cryptographic reference asset eg. ETH will go up, plays that role. Unlike issuers, speculators have no option to default on their side of the bargain because the hedging contract holds their funds in escrow.
Note that this approach is not fully decentralized, because a trusted source is still needed to provide the price ticker, although arguably even still this is a massive improvement in terms of reducing infrastructure requirements unlike being an issuer, issuing a price feed requires no licenses and can likely be categorized as free speech and reducing the potential for fraud. Identity and Reputation Systems The earliest alternative cryptocurrency of all, Namecoin , attempted to use a Bitcoin-like blockchain to provide a name registration system, where users can register their names in a public database alongside other data.
The major cited use case is for a DNS system, mapping domain names like "bitcoin. Other use cases include email authentication and potentially more advanced reputation systems. Here is the basic contract to provide a Namecoin-like name registration system on Ethereum: def register name, value : if!
Anyone can register a name with some value, and that registration then sticks forever. A more sophisticated name registration contract will also have a "function clause" allowing other contracts to query it, as well as a mechanism for the "owner" ie. One can even add reputation and web-of-trust functionality on top.
Decentralized File Storage Over the past few years, there have emerged a number of popular online file storage startups, the most prominent being Dropbox, seeking to allow users to upload a backup of their hard drive and have the service store the backup and allow the user to access it in exchange for a monthly fee. However, at this point the file storage market is at times relatively inefficient; a cursory look at various existing solutions shows that, particularly at the "uncanny valley" GB level at which neither free quotas nor enterprise-level discounts kick in, monthly prices for mainstream file storage costs are such that you are paying for more than the cost of the entire hard drive in a single month.
Ethereum contracts can allow for the development of a decentralized file storage ecosystem, where individual users can earn small quantities of money by renting out their own hard drives and unused space can be used to further drive down the costs of file storage.
The key underpinning piece of such a device would be what we have termed the "decentralized Dropbox contract". This contract works as follows. First, one splits the desired data up into blocks, encrypting each block for privacy, and builds a Merkle tree out of it. One then makes a contract with the rule that, every N blocks, the contract would pick a random index in the Merkle tree using the previous block hash, accessible from contract code, as a source of randomness , and give X ether to the first entity to supply a transaction with a simplified payment verification-like proof of ownership of the block at that particular index in the tree.
When a user wants to re-download their file, they can use a micropayment channel protocol eg. An important feature of the protocol is that, although it may seem like one is trusting many random nodes not to decide to forget the file, one can reduce that risk down to near-zero by splitting the file into many pieces via secret sharing, and watching the contracts to see each piece is still in some node's possession.
If a contract is still paying out money, that provides a cryptographic proof that someone out there is still storing the file. The members would collectively decide on how the organization should allocate its funds. Methods for allocating a DAO's funds could range from bounties, salaries to even more exotic mechanisms such as an internal currency to reward work. This essentially replicates the legal trappings of a traditional company or nonprofit but using only cryptographic blockchain technology for enforcement.
The requirement that one person can only have one membership would then need to be enforced collectively by the group. A general outline for how to code a DAO is as follows. The simplest design is simply a piece of self-modifying code that changes if two thirds of members agree on a change. Although code is theoretically immutable, one can easily get around this and have de-facto mutability by having chunks of the code in separate contracts, and having the address of which contracts to call stored in the modifiable storage.
In a simple implementation of such a DAO contract, there would be three transaction types, distinguished by the data provided in the transaction: [0,i,K,V] to register a proposal with index i to change the address at storage index K to value V [1,i] to register a vote in favor of proposal i [2,i] to finalize proposal i if enough votes have been made The contract would then have clauses for each of these. It would maintain a record of all open storage changes, along with a list of who voted for them.
It would also have a list of all members. When any storage change gets to two thirds of members voting for it, a finalizing transaction could execute the change. A more sophisticated skeleton would also have built-in voting ability for features like sending a transaction, adding members and removing members, and may even provide for Liquid Democracy -style vote delegation ie.
This design would allow the DAO to grow organically as a decentralized community, allowing people to eventually delegate the task of filtering out who is a member to specialists, although unlike in the "current system" specialists can easily pop in and out of existence over time as individual community members change their alignments.
An alternative model is for a decentralized corporation, where any account can have zero or more shares, and two thirds of the shares are required to make a decision. A complete skeleton would involve asset management functionality, the ability to make an offer to buy or sell shares, and the ability to accept offers preferably with an order-matching mechanism inside the contract. Delegation would also exist Liquid Democracy-style, generalizing the concept of a "board of directors". Further Applications 1.
Savings wallets. Suppose that Alice wants to keep her funds safe, but is worried that she will lose or someone will hack her private key. Alice and Bob together can withdraw anything. If Alice's key gets hacked, she runs to Bob to move the funds to a new contract. If she loses her key, Bob will get the funds out eventually. If Bob turns out to be malicious, then she can turn off his ability to withdraw.
Crop insurance. One can easily make a financial derivatives contract but using a data feed of the weather instead of any price index. If a farmer in Iowa purchases a derivative that pays out inversely based on the precipitation in Iowa, then if there is a drought, the farmer will automatically receive money and if there is enough rain the farmer will be happy because their crops would do well.
This can be expanded to natural disaster insurance generally. A decentralized data feed. For financial contracts for difference, it may actually be possible to decentralize the data feed via a protocol called " SchellingCoin ". SchellingCoin basically works as follows: N parties all put into the system the value of a given datum eg. Everyone has the incentive to provide the answer that everyone else will provide, and the only value that a large number of players can realistically agree on is the obvious default: the truth.
Smart multisignature escrow. Bitcoin allows multisignature transaction contracts where, for example, three out of a given five keys can spend the funds. Additionally, Ethereum multisig is asynchronous - two parties can register their signatures on the blockchain at different times and the last signature will automatically send the transaction.
Cloud computing. The EVM technology can also be used to create a verifiable computing environment, allowing users to ask others to carry out computations and then optionally ask for proofs that computations at certain randomly selected checkpoints were done correctly.
This allows for the creation of a cloud computing market where any user can participate with their desktop, laptop or specialized server, and spot-checking together with security deposits can be used to ensure that the system is trustworthy ie. Although such a system may not be suitable for all tasks; tasks that require a high level of inter-process communication, for example, cannot easily be done on a large cloud of nodes.
Other tasks, however, are much easier to parallelize; projects like SETI home, folding home and genetic algorithms can easily be implemented on top of such a platform. Peer-to-peer gambling. Any number of peer-to-peer gambling protocols, such as Frank Stajano and Richard Clayton's Cyberdice , can be implemented on the Ethereum blockchain. The simplest gambling protocol is actually simply a contract for difference on the next block hash, and more advanced protocols can be built up from there, creating gambling services with near-zero fees that have no ability to cheat.
Prediction markets. Provided an oracle or SchellingCoin, prediction markets are also easy to implement, and prediction markets together with SchellingCoin may prove to be the first mainstream application of futarchy as a governance protocol for decentralized organizations. On-chain decentralized marketplaces, using the identity and reputation system as a base. The motivation behind GHOST is that blockchains with fast confirmation times currently suffer from reduced security due to a high stale rate - because blocks take a certain time to propagate through the network, if miner A mines a block and then miner B happens to mine another block before miner A's block propagates to B, miner B's block will end up wasted and will not contribute to network security.
Thus, if the block interval is short enough for the stale rate to be high, A will be substantially more efficient simply by virtue of its size. With these two effects combined, blockchains which produce blocks quickly are very likely to lead to one mining pool having a large enough percentage of the network hashpower to have de facto control over the mining process.
As described by Sompolinsky and Zohar, GHOST solves the first issue of network security loss by including stale blocks in the calculation of which chain is the "longest"; that is to say, not just the parent and further ancestors of a block, but also the stale descendants of the block's ancestor in Ethereum jargon, "uncles" are added to the calculation of which block has the largest total proof-of-work backing it.
To solve the second issue of centralization bias, we go beyond the protocol described by Sompolinsky and Zohar, and also provide block rewards to stales: a stale block receives Transaction fees, however, are not awarded to uncles. It cannot be an ancestor of B An uncle must be a valid block header, but does not need to be a previously verified or even valid block An uncle must be different from all uncles included in previous blocks and all other uncles included in the same block non-double-inclusion For every uncle U in block B, the miner of B gets an additional 3.
This limited version of GHOST, with uncles includable only up to 7 generations, was used for two reasons. First, unlimited GHOST would include too many complications into the calculation of which uncles for a given block are valid. Second, unlimited GHOST with compensation as used in Ethereum removes the incentive for a miner to mine on the main chain and not the chain of a public attacker.
Fees Because every transaction published into the blockchain imposes on the network the cost of needing to download and verify it, there is a need for some regulatory mechanism, typically involving transaction fees, to prevent abuse. The default approach, used in Bitcoin, is to have purely voluntary fees, relying on miners to act as the gatekeepers and set dynamic minimums.
This approach has been received very favorably in the Bitcoin community particularly because it is "market-based", allowing supply and demand between miners and transaction senders determine the price. The problem with this line of reasoning is, however, that transaction processing is not a market; although it is intuitively attractive to construe transaction processing as a service that the miner is offering to the sender, in reality every transaction that a miner includes will need to be processed by every node in the network, so the vast majority of the cost of transaction processing is borne by third parties and not the miner that is making the decision of whether or not to include it.
Hence, tragedy-of-the-commons problems are very likely to occur. However, as it turns out this flaw in the market-based mechanism, when given a particular inaccurate simplifying assumption, magically cancels itself out. The argument is as follows. Suppose that: A transaction leads to k operations, offering the reward kR to any miner that includes it where R is set by the sender and k and R are roughly visible to the miner beforehand.
An operation has a processing cost of C to any node ie. A miner would be willing to process a transaction if the expected reward is greater than the cost. Note that R is the per-operation fee provided by the sender, and is thus a lower bound on the benefit that the sender derives from the transaction, and NC is the cost to the entire network together of processing an operation.
Hence, miners have the incentive to include only those transactions for which the total utilitarian benefit exceeds the cost. However, there are several important deviations from those assumptions in reality: The miner does pay a higher cost to process the transaction than the other verifying nodes, since the extra verification time delays block propagation and thus increases the chance the block will become a stale.
There do exist nonmining full nodes. The mining power distribution may end up radically inegalitarian in practice. Speculators, political enemies and crazies whose utility function includes causing harm to the network do exist, and they can cleverly set up contracts where their cost is much lower than the cost paid by other verifying nodes. Specifically: blk. There is another factor disincentivizing large block sizes in Bitcoin: blocks that are large will take longer to propagate, and thus have a higher probability of becoming stales.
In Ethereum, highly gas-consuming blocks can also take longer to propagate both because they are physically larger and because they take longer to process the transaction state transitions to validate. This delay disincentive is a significant consideration in Bitcoin, but less so in Ethereum because of the GHOST protocol; hence, relying on regulated block limits provides a more stable baseline.
Computation And Turing-Completeness An important note is that the Ethereum virtual machine is Turing-complete; this means that EVM code can encode any computation that can be conceivably carried out, including infinite loops. EVM code allows looping in two ways. Second, contracts can call other contracts, potentially allowing for looping through recursion. This naturally leads to a problem: can malicious users essentially shut miners and full nodes down by forcing them to enter into an infinite loop?
The issue arises because of a problem in computer science known as the halting problem: there is no way to tell, in the general case, whether or not a given program will ever halt. As described in the state transition section, our solution works by requiring a transaction to set a maximum number of computational steps that it is allowed to take, and if execution takes longer computation is reverted but fees are still paid.
Messages work in the same way. To show the motivation behind our solution, consider the following examples: An attacker creates a contract which runs an infinite loop, and then sends a transaction activating that loop to the miner. The miner will process the transaction, running the infinite loop, and wait for it to run out of gas.
Even though the execution runs out of gas and stops halfway through, the transaction is still valid and the miner still claims the fee from the attacker for each computational step. An attacker creates a very long infinite loop with the intent of forcing the miner to keep computing for such a long time that by the time computation finishes a few more blocks will have come out and it will not be possible for the miner to include the transaction to claim the fee.
However, the attacker will be required to submit a value for STARTGAS limiting the number of computational steps that execution can take, so the miner will know ahead of time that the computation will take an excessively large number of steps. An attacker sees a contract with code of some form like send A,contract.
The contract author does not need to worry about protecting against such attacks, because if execution stops halfway through the changes get reverted. A financial contract works by taking the median of nine proprietary data feeds in order to minimize risk. An attacker takes over one of the data feeds, which is designed to be modifiable via the variable-address-call mechanism described in the section on DAOs, and converts it to run an infinite loop, thereby attempting to force any attempts to claim funds from the financial contract to run out of gas.
However, the financial contract can set a gas limit on the message to prevent this problem. With this system, the fee system described and the uncertainties around the effectiveness of our solution might not be necessary, as the cost of executing a contract would be bounded above by its size. Additionally, Turing-incompleteness is not even that big a limitation; out of all the contract examples we have conceived internally, so far only one required a loop, and even that loop could be removed by making 26 repetitions of a one-line piece of code.
Given the serious implications of Turing-completeness, and the limited benefit, why not simply have a Turing-incomplete language? In reality, however, Turing-incompleteness is far from a neat solution to the problem. C call C50 ; call C50 ; C run one step of a program and record the change in storage Now, send a transaction to A.
Thus, in 51 transactions, we have a contract that takes up computational steps. Miners could try to detect such logic bombs ahead of time by maintaining a value alongside each contract specifying the maximum number of computational steps that it can take, and calculating this for contracts calling other contracts recursively, but that would require miners to forbid contracts that create other contracts since the creation and execution of all 26 contracts above could easily be rolled into a single contract.
Another problematic point is that the address field of a message is a variable, so in general it may not even be possible to tell which other contracts a given contract will call ahead of time. Hence, all in all, we have a surprising conclusion: Turing-completeness is surprisingly easy to manage, and the lack of Turing-completeness is equally surprisingly difficult to manage unless the exact same controls are in place - but in that case why not just let the protocol be Turing-complete?
Each node communicates with a relatively small subset of the network—its "peers". Whenever a node wishes to include a new transaction in the blockchain, it sends a copy of the transaction to each of its peers, who then send a copy to each of their peers, and so on. In this way, it propagates throughout the network. Certain nodes, called miners, maintain a list of all of these new transactions and use them to create new blocks, which they then send to the rest of the network.
Whenever a node receives a block, it checks the validity of the block and of all of the transactions therein and, if it finds the block to be valid, adds it to its blockchain and executes all of those transactions. Since block creation and broadcasting are permissionless, a node may receive multiple blocks competing to be the successor to a particular block. The node keeps track of all of the valid chains that result from this and regularly drops the shortest one: According to the Ethereum protocol, the longest chain at any given time is to be considered the canonical one.
Ether Ether ETH is the cryptocurrency generated in accordance with the Ethereum protocol as a reward to miners in a proof-of-work system for adding blocks to the blockchain. This is known as the block reward. Additionally, ether is the only currency accepted by the protocol as payment for a transaction fee, which also goes to the miner. The block reward together with the transaction fees provide the incentive to miners to keep the blockchain growing i.
Therefore, ETH is fundamental to the operation of the network. Ether may be "sent" from one account to another via a transaction, which simply entails subtracting the amount to be sent from the sender's balance and adding the same amount to the recipient's balance.
Both types have an ETH balance, may send ETH to any account, may call any public function of a contract or create a new contract, and are identified on the blockchain and in the state by an account address. For a transaction to be valid, it must be signed using the sending account's private key, the character hexadecimal string from which the account's address is derived.
Importantly, this algorithm allows one to derive the signer's address from the signature without knowing the private key. Contracts are the only type of account that has associated code a set of functions and variable declarations and contract storage the values of the variables at any given time. A contract function may take arguments and may have return values.
In addition to control flow statements, the body of a function may include instructions to send ETH, read from and write to the contract's storage, create temporary storage memory that vanishes at the end of the function, perform arithmetic and hashing operations, call the contract's own functions, call public functions of other contracts, create new contracts, and query information about the current transaction or the blockchain.
In hexadecimal, two digits represent a byte, and so addresses contain 40 hexadecimal digits, e. Contract addresses are in the same format, however, they are determined by sender and creation transaction nonce. It includes a stack , memory, and the persistent storage for all Ethereum accounts including contract code. The EVM is stack-based, in that most instructions pop operands from the stack and push the result to the stack. The EVM is designed to be deterministic on a wide variety of hardware and operating systems , so that given a pre-transaction state and a transaction, each node produces the same post-transaction state, thereby enabling network consensus.
Each type of operation which may be performed by the EVM is hardcoded with a certain gas cost, which is intended to be roughly proportional to the amount of resources computation and storage a node must expend to perform that operation. When a sender creates a transaction, the sender must specify a gas limit and gas price. The gas limit is the maximum amount of gas the sender is willing to use in the transaction, and the gas price is the amount of ETH the sender wishes to pay to the miner per unit of gas used.
The higher the gas price, the more incentive a miner has to include the transaction in their block, and thus the quicker the transaction will be included in the blockchain. The sender buys the full amount of gas i.
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