Provable — Randomness Oracle

How the Oraclize random number generator works

One particularly interesting approach by Provable is the usage of a hardware security device, namely the Ledger Nano S. It uses a trusted execution environment to generate random numbers and provides a Provable Connector Contract as interface.

How to use the Provable Randomness Oracle?

Use the example provided, the update() function will retrieve a new random number from the oracle by calling oraclize_newRandomDSQuery() . The calling contract needs to have a __callback function defined for Provable to send the reply to. Beware that for maximum security, you really want two modifcations to the example:

  1. Actually store the query returned oraclize_newRandomDSQuery in the contract and verify it’s matching the query id returned to the callback function.
  2. Remove the require(msg.sender == oraclize_cbAddress()) as it allows Provable to ransom a winning player.

Please also consider front-running when using the random oracle. This is easily possible by designing the system similarly to the future blockhash I mentioned in the previous post.

How does the Provable Randomness Oracle work in detail?

Okay, so let us go in the details, if you are curious on how this actually works. We will call the provider of such a service, e.g., Provable, the data carrier. The Connector Contract expects four parameters:

  • A commitment nonce which must not be known to the data carrier before submitting the request.
  • A time dT in seconds to represent the minimal delay required between submission of the request and response.
  • The public key of the device be to used.
  • The number of random bytes to return, between 1 and 32.

The Ledger Nano S allows running custom applications on it. However, it has some challenging limitations, e.g., a very small volatile as well as a very small non-volatile memory. The proposed secure application by Provable is designed to cope with the Ledger Nano S’ limitations by minimizing the required memory as well as reducing the number of writes. The secure application needs three features for its security:

  • A timer for verifying that the provided time dT has passed.
  • A tamper-resistant memory to hold the private keys without being accessible to the data carrier.
  • A management component for signing and verifying with the public key and the secret private key.

The API, i.e., the functions that can be accessed from the outside, contains four endpoints:

  • Initialization: Run once for the setup.
  • Query Insertion: A new query for a random value can be inserted.
  • Volatile Memory State Import/Export: Export device memory and later import it.
  • Query Execution: The actual generation of the random value.

Initialization: Firstly, depending on the current application’s code and device state, an elliptic curve key pair is generated. Doing that, the generated application session key is bound to an exact behavior. Any changes to the behavior, e.g., updating the firmware or re-installing the application, destroy the application’s session key and would therefore require the generation of a new key. Thus, an existing session key can be verified by looking at the device’s firmware and application code.

Query Insertion: This function enables the insertion of new queries into the tamper-resistant state. It is essential for the security that only one query per query id exists, since with the addition of multiple queries per query id a malicious random data provider can run multiple queries to predict user queries. Storing a mapping for each query id to query data would be impossible with the limited storage available on a ledger. That is why there is an authenticated data structure. It enables the authentication of increasing amounts of data without an increasing demand for storage. The proving is done by a host application which keeps the whole data structure in storage. Only the verification is done on the device itself. The chosen data structure is similar to the merkle patricia trie used by Ethereum. In specific, the tree is characterized as follows:

  • All inner nodes have 16 children.
  • All inner nodes contain the hash over all their children’s entries.
  • All leaf nodes contain either the hashed query parameters or they are empty.
  • The tree has a fixed depth of 64.
  • Each query id is represented by a 64 character hash of that id which is the key for traversing the tree.

The key aspect of such a tree is that only the hash of the root node needs to be stored. The leaf nodes contain the hash over all query parameters, i.e., H(current time, commitment nonce, dT, number of random bytes to return). When inserting new queries, the following steps are required:

  1. Create the leaf node for the query by computing the hash over all query parameters.
  2. Compute the hash of the query id and reverse it.
  3. Traverse the reversed key and at each node: Use the leaf node hash or subsequently computed node hashes. Compute the node hash without the added new query leaf node. If hashes of other nodes are required, they can be retrieved from the host application. Likewise, compute the node hash with the added new query leaf node. Store the newly computed hashes in temporary variables, oldHash and newHash.
  4. Once reached the root node, compare oldHash with the stored root hash. If they match, replace the stored root hash with newHash.

Volatile Memory State Import/Export: A device might be used to serve multiple host applications. Additionally, it might be restarted intentionally or unintentionally. In consequence, functions for importing and exporting the current device state are necessary. For exporting a state, the device signs its current state with its session’s private key. Additionally, it includes its current storage nonce into the signed data. When importing a state, the signature is verified and the storage nonce must match the internal value for the storage nonce. That implies data carriers may not import older device states but only the last one.

Query Execution: Any query can only be executed once: queryInsertionTime + dt < currentTime. The execution itself involves signing the hash of the query parameters with its session’s private key. The number of random bytes requested represent the truncation of the computed signature.

Usage in Ethereum: In a smart contract, the procedure may work the following: The smart contract requiring a random number computes a commitment nonce which is unknown to the data carrier prior to the query submission. See below for the exact procedure for computing such a nonce. The nonce is passed to a data carrier smart contract (the Connector Contract) along with dT, the number of random bytes required and the device’s session public key. The data carrier smart contract subsequently does the following:

  1. Increments the query id counter for the requesting smart contract’s address.
  2. Subtracts a fee from the sender (the requesting smart contract).
  3. Computes the hash over the requesting smart contract’s address and the query id counter.
  4. Triggers the query insertion into the ledger device by emitting a log event.

After a successful query insertion followed by a successful query execution, the generated random bytes along with the query id and the proof are sent to a predefined callback function inside the requesting smart contract. The sent random bytes are only accepted and used once the proof is verified:

  1. The random number must have been generated on a ledger device. The ledger proof can verify this.
  2. The commitment nonce inside the proof must match the original commitment nonce.
  3. The application code hash must match a whitelisted application code hash.

Security Analysis: Three properties must hold for the security of such applications:

  1. The session’s private key must be unknown to the data carrier.
  2. The commitment nonce must be unknown prior to query insertion to the data carrier.
  3. The device application must function in the described way.

We will further examine all three properties. The first property is critical. With possession of the private key, a data carrier could run the query execution at any time without the ledger device. That would imply a data carrier can predict the random value for every query without any effort. All the security guarantees rely on the fact that the private key is unknown. Gaining knowledge of the device key is difficult, but not impossible. It relies on the tamper-state memory which may be possible. Nevertheless, the process of reading the memory is generally extremely difficult and expensive without any guarantee of success. Costs can easily exceed hundreds of thousands or millions of US dollars. For very successful applications, these costs may still be too low. In such a scenario, application developers may rely on multiple different devices for their random value generation, linearly increasing the costs for a successful attack.

The second property is important since otherwise a data carrier may compute the result before a user submits his query. It can be prevented with the proper choice for the query parameters. Since there can only be one query per query id, any attempt to successfully compute random values beforehand, must correctly guess all query parameters. The data carrier has only one chance to guess these parameters correctly. In case he fails to do so, the attempt may be detected by users after not receiving a result for their query.

Choosing a commitment nonce which may not be predicted by the data carrier can be done the following: In the case of Ethereum, a user may choose the hash over all block variables, i.e., the current block’s coinbase, timestamp, gas limit and the last block hash. Additionally, he chooses a dT that is higher than the block time in Ethereum. This yields a very difficult prediction of the commitment nonce for the data carrier. The data carrier cannot predict the block variables unless he colludes with miners. Even in that case, they have only one chance of correctly predicting all variables and a failure would be detectable.

Finally, the application code for the device must be verified manually. That process involves generating the application’s code hash for every given source code and comparing it to the whitelisted hashes. Each application’s source code must bijectively match one of the whitelisted hashes. Furthermore, every given source code must be checked for its logic. We believe this to be a long and error-prone process, especially after trying to read the exemplary implementation’s source code ourselves. Though, increased code readability and high-quality documentation may help in this process.

Of note, a data carrier may choose not to submit the results of successful query executions. He may do so for unknown reasons, but an application can secure itself from giving monetary incentives to the data carrier by not refunding users after a data carrier has stopped submitted the result for a given query. Furthermore, there is no denial-of-service protection given and the data carrier is a single point of failure. Consequently, there should not be any timeout in requesting applications.

In addition, ransoming a winning player may be an issue depending on the implementation of using applications. If the callback for submitting queries is restricted only to be used by the data carrier, he could go to a winning player. By providing the proof for the given query, he could prove to him that he is in fact a winner. Due to the callback being restricted only to the data carrier, the data carrier may only publish the result after receiving money from the winning player. Allowing the submission of queries for anyone mitigates this risk. Firstly, it poses no threat to allow the submission for anyone, because only results with valid proofs will be accepted. Secondly, it allows a ransomed winning player to submit the proof himself. To prove to him that he is in fact the winning player, the data carrier must provide the whole proof. Otherwise, the ransomed player cannot be sure that he is really the winner. At the time of the writing, the exemplary application implementation of Provable is vulnerable to this attack since it does in fact restrict the submission of the result only to the data carrier.

Conclusion

We have looked at two methods for multi-party randomness in Solidity. While the commitment approach is not very useful for most real-world use cases, using an oracle is your best bet at this time. However, it is most definitely not ideal as it is a centralized solution. Unfortunately, there is no perfect approach at this point in time.

The future: This might change quite soon. With the introduction of ETH2.0 and their Proof of Stake algorithm, the randomness generation may be used by smart contracts as well. We will discuss the ETH2.0 approach as well as other POS algorithms and how that would help randomness in smart contracts in a later blog post.


Markus Waas

Solidity Developer

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    You know what an ERC-20 is, you probably have created your own versions of it several times (if not, have a look at: ERC-20). But how would you start in 2020 using the latest tools? Let's create a new ERC-2020 token contract with some basic functionality which focuses on simplicity and latest...

  • hiring

    How to get a Solidity developer job?

    There are many ways to get a Solidity job and it might be easier than you think!

    You have mastered the basics of Solidity, created your first few useful projects and now want to get your hands on some real-world projects. Getting a Solidity developer job might be easier than you think. There are generally plenty of options to choose from and often times not a lot of...

  • People making fun

    Design Pattern Solidity: Mock contracts for testing

    Why you should make fun of your contracts

    Mock objects are a common design pattern in object-oriented programming. Coming from the old French word 'mocquer' with the meaning of 'making fun of', it evolved to 'imitating something real' which is actually what we are doing in programming. Please only make fun of your smart contracts if you...

  • React and Ethereum

    Kickstart your Dapp frontend development with create-eth-app

    An overview on how to use the app and its features

    Last time we looked at the big picture of Solidity and already mentioned the create-eth-app. Now you will find out how to use it, what features are integrated and additional ideas on how to expand on it. Started by Paul Razvan Berg, the founder of sablier, this app will kickstart your frontend...

  • Solidity Overview

    The big picture of Solidity and Blockchain development in 2020

    Overview of the most important technologies, services and tools that you need to know

    Now, I do not know about you, but I remember when I first started with Solidity development being very confused by all the tools and services and how they work in connection with one another. If you are like me, this overview will help you understand the big picture of Solidity development. As I...

  • Design Pattern Solidity: Free up unused storage

    Why you should clean up after yourself

    You may or may not be used to a garbage collectors in your previous programming language. There is no such thing in Solidity and even if there was a similar concept, you would still be better off managing state data yourself. Only you as a programmer can know exactly which data will not be used...

  • How to setup Solidity Developer Environment on Windows

    What you need to know about developing on Windows

    Using Windows for development, especially for Solidity development, can be a pain sometimes, but it does not have to be. Once you have configured your environment properly, it can actually be extremely efficient and Windows is a very, very stable OS, so your overall experience can be amazing. The...

  • Avoiding out of gas for Truffle tests

    How you do not have to worry about gas in tests anymore

    You have probably seen this error message a lot of times: Error: VM Exception while processing transaction: out of gas Disclaimer : Unfortunately, this does not always actually mean what it is saying when using Truffle , especially for older versions. It can occur for various reasons and might be...

  • Design Pattern Solidity: Stages

    How you can design stages in your contract

    Closely related to the concept of finite-state machines, this pattern will help you restrict functions in your contract. You will find a lot of situations where it might be useful. Any time a contract should allow function calls only in certain stages. Let's look at an example: contract Pool {...

  • Web3 1.2.5: Revert reason strings

    How to use the new feature

    A new Web3 version was just released and it comes with a new feature that should make your life easier. With the latest version 1.2.5, you can now see the the revert reason if you use the new handleRevert option. You can activate it easily by using web3.eth.handleRevert = true . Now when you use...

  • Gaining back control of the internet

    How Ocelot is decentralizing cloud computing

    I recently came across an ambitious company that will completely redefine the way we are using the internet. Or rather, the way we are using its underlying infrastructure which ultimately is the internet. While looking at their offering, I also learned how to get anonymous cloud machines, you...

  • Devcon 5 - Review

    Impressions from the conference

    I had a lot to catch up on after Devcon. Also things didn't go quite as planned, so please excuse my delayed review! This year's Devcon was certainly stormy with a big typhoon warning already on day 1. Luckily (for us, not the people in Tokyo), it went right past Osaka. Nevertheless, a lot of...

  • Devcon 5 - Information, Events, Links, Telegram

    What you need to know

    Devcon 5 is coming up soon and there are already lots of events available, information about Osaka and more. Here is a short overview: Events Events Calendar Events Google Docs Events Kickback Most events are in all three, but if you really want to see all, you will have to look at all three...

  • Design Pattern Solidity: Off-chain beats on-chain

    Why you should do as much as possible off-chain

    As you might have realized, Ethereum transactions are anything but cheap. In particular, if you are computing complex things or storing a lot of data. That means sometimes we cannot put all logic inside Solidity. Instead, we can utilize off-chain computations to help us. A very simple example...

  • Design Pattern Solidity: Initialize Contract after Deployment

    How to use the Initializable pattern

    There are a few reasons why you might want to initialize a contract after deployment and not directly by passing constructor arguments. But first let's look at an example: contract MyCrowdsale { uint256 rate; function initialize(uint256 _rate) public { rate = _rate; } } What's the advantage over...

  • Consensys Blockchain Jobs Report

    What the current blockchain job market looks like

    Consensys published their blockchain jobs report which you can checkout in their Blockchain Developer Job Kit. The most interesting aspects are Blockchain developer jobs have been growing at a rate of 33x of the previous year according to LinkedIns jobs report Typical salary is about...

  • Solidity Design Patterns: Multiply before Dividing

    Why the correct order matters!

    There has been a lot of progress since the beginning of Ethereum about best practices in Solidity. Unfortunately, I have the feeling that most of the knowledge is within the circle of experienced people and there aren’t that many online resources about it. That is why I would like to start this...

  • Devcon 5 Applications closing in one week

    Devcon 5 Applications closing

    Watch out for the Devcon 5 applications. You only have one week left to apply either as Buidler Student Scholarship Press Devcon is by far the biggest and most impressive Ethereum conference in the world. And it's full of developers! I am especially excited about the cool location this year in...

  • Randomness and the Blockchain

    How to achieve secure randomness for Solidity smart contracts?

    When we talk about randomness and blockchain, these are really two problems: 1. How to generate randomness in smart contracts? 2. How to produce randomness for proof-of-stake (POS) systems? Or more generally, how to produce trusted randomness in public distributed systems? There is some overlap...