Scrypto is an open source cryptographic toolkit designed to make it easier and safer for developers to use cryptography in their applications.
It was extracted from Scorex, opensource modular blockchain & cryptocurrency framework.
Public Domain.
If you want to check benchmarks for authenticated AVL+ trees, please visit dedicated repository. Use the repository as code examples for the trees also, though one code example is provided in "Authenticated Data Structures" section below.
Scrypto is available on Sonatype for Scala 2.12 / 2.13:
resolvers += "Sonatype Releases" at "https://oss.sonatype.org/content/repositories/releases/"
You can use Scrypto in your sbt project by simply adding the following dependency to your build file:
libraryDependencies += "org.scorexfoundation" %% "scrypto" % "3.0.0"
Supported hash algorithms are:
 Blake2b
 Keccak
 Sha
 Whirlpool
 Skein
 Stribog
Take a look at CryptographicHash interface and use supported hash algorithms like
Keccak512("some string or bytes")
All provided hash functions are secure, and their implementations are thread safe.
You can create commutative hash from any hash function with CommutativeHash
case class like CommutativeHash(Sha256)
.
A hash function h is commutative if h(x,y)==h(y,x) , for all x and y.
Scrypto has implementations of few binarytotext encoding schemes:
 Base16
 Base58
 Base64
Example:
val encoded = Base64.encode(data)
val restored = Base64.decode(encoded)
restored shouldBe data
Scrypto supports following elliptic curves:
 Curve25519(& Ed25519)
Example:
val curveImpl = new Curve25519
val keyPair = curveImpl.createKeyPair()
val sig = curveImpl.sign(keyPair._1, message)
assert(curveImpl.verify(sig, message, keyPair._2))
Note on security: Scrypto provides a simple Scala wrapper for Curve25519Java by Whisper Systems, so has the same security properties. JDK's SecureRandom is used to obtain seed bytes.
Scrypto supports twoparty authenticated AVL+ trees with the batching compression support and guaranteed verifier efficiency, as described in http://eprint.iacr.org/2016/994.
The implementation can be found in the scorex.crypto.authds.avltree.batch
package.
The overall approach is as follows. The prover has a data structure of (key, value) pairs
and can perform operations on it using performOneOperation
method. An operation (see scorex.crypto.authds.avltree.batch.Operation
) is either a lookup or a modification.
We provide sample modifications (such as insertions, removals, and additions/subtractions from the value of a given key), but users of this code may define their own (such as subtractions that allow negative values, unlike our subtractions). A modification may be defined to fail under certain conditions (e.g., a deletion of a key that is not there, or a subtraction that results in a negative value), in which case the tree is not modified. If the operation succeeds, it returns the value associated with the key before the operation was performed. The prover can compute the digest of the current state of the data structure via the digest
method. At any point the prover may use generateProof
, which will produce a proof covering the batch of operations (except the ones that failed) since the last generateProof
.
The verifier is constructed from the digest that preceeded the latest batch of operations and the proof for the latest batch. The verifier can also be given optional parameters for the maximum number of operations (and at most how many of those are deletions) in order to guarantee a bound on the verifier running time in case of a malicious proof, thus mitigating denial of service attacks. Once constructed, the verifier can replay the same sequence of operations to compute the new digest and to be assured that the operations do not fail and their return values are correct. Note that the verifier is not assured that the sequence of operations is the same as the one the prover performedit is assumed that the prover and verifier agree on the sequence of operations (twoparty authenticated data structures are useful when the prover and verifier agree on the sequence of operations). However, if the verifier digest matches the prover digest after the sequence of operations, then the verifier is assured that the state of the data structure is the same, regardless of what sequence of operations led to this state.
We also provide unauthenticatedLookup
for the prover, in order to allow the prover to look up values in the data structure without affecting the proof.
Here are code examples for generating proofs and checking them. In this example we demonstrate two batches of operations, starting with the empty tree. In the first batch, a prover inserts three values into the tree; in the second batch, the prover changes the first value, attempts to subtract too much from the second one, which fails, looks up the third value, and attempts to delete a nonexisting value, which also fails. We use 1byte keys for simplicity; in a real deployment, keys would be longer.
 First, we create a prover and get an initial digest from it (in a real application, this value is a public constant because anyone, including verifiers, can compute it by using the same two lines of code)
import scorex.utils.Longs
import scorex.crypto.authds.{ADKey, ADValue}
import scorex.crypto.authds.avltree.batch._
import scorex.crypto.hash.{Blake2b256, Digest32}
val prover = new BatchAVLProver(keyLength = 1, valueLengthOpt = Some(8))
val initialDigest = prover.digest
 Second, we create the first batch of tree modifications, inserting keys 1, 2, and 3 with values 10, 20, and 30. We use
com.google.common.primitives.Longs.toByteArray
to get 8byte values out of longs.
val key1 = Array(1:Byte)
val key2 = Array(2:Byte)
val key3 = Array(3:Byte)
val op1 = Insert(ADKey @@ key1, ADValue @@ Longs.toByteArray(10))
val op2 = Insert(ADKey @@ key2, ADValue @@ Longs.toByteArray(20))
val op3 = Insert(ADKey @@ key3, ADValue @@ Longs.toByteArray(30))
 The prover applies the three modifications to the empty tree, obtains the first batch proof, and announces the next digest
digest1
.
prover.performOneOperation(op1) // Returns Success(None)
prover.performOneOperation(op2) // Returns Success(None)
prover.performOneOperation(op3) // Returns Success(None)
val proof1 = prover.generateProof()
val digest1 = prover.digest

A proof is just an array of bytes, so you can immediately send it over a wire or save it to a disk.

Next, the prover attempts to perform five more modifications: changing the first value to 50, subtracting 40 from the second value (which will fail, because our UpDateLongBy operation is designed to fail on negative values), looking up the third value, deleting the key 5 (which will also fail, because key 5 does not exist), and deleting the third value. After the four operations, the prover obtains a second proof, and announces the new digest
digest2
val op4 = Update(ADKey @@ key1, ADValue @@ Longs.toByteArray(50))
val op5 = UpdateLongBy(ADKey @@ key2, 40)
val op6 = Lookup(ADKey @@ key3)
val op7 = Remove(ADKey @@ Array(5:Byte))
val op8 = Remove(ADKey @@ key3)
prover.performOneOperation(op4) // Returns Success(Some(Longs.toByteArray(10)))
// Here we can, for example, perform prover.unauthenticatedLookup(key1) to get 50
// without affecting the proof or anything else
prover.performOneOperation(op5) // Returns Failure
prover.performOneOperation(op6) // Returns Success(Some(Longs.toByteArray(30)))
prover.performOneOperation(op7) // Returns Failure
prover.performOneOperation(op8) // Returns Success(Some(Longs.toByteArray(30)))
val proof2 = prover.generateProof() // Proof only for op4 and op6
val digest2 = prover.digest

We now verify the proofs. For each batch, we first construct a verifier using the digest that preceded the batch and the proof of the batch; we also supply an upper bound on the number of operations in the batch and an upper bound on how many of those operations are deletions. Note that the number of operations can be None, in which case there is no guaranteed running time bound; furthermore, the number of deletions can be None, in which case the guaranteed running time bound is not as small as it can be if a good upper bound on the number of deletion is supplied.

Once the verifier for a particular batch is constructed, we perform the same operations as the prover, one by one (but not the ones that failed for the prover). If verification fails at any point (at construction time or during an operation), the verifier digest will equal None from that point forward, and no further verifier operations will change the digest. Else, the verifier's new digest is the correct one for the tree as modified by the verifier. Furthermore, if the verifier performed the same modifications as the prover, then the verifier and prover digests will match.
val verifier1 = new BatchAVLVerifier[Digest32, Blake2b256.type](initialDigest, proof1, keyLength = 1, valueLengthOpt = Some(8), maxNumOperations = Some(2), maxDeletes = Some(0))
verifier1.performOneOperation(op1) // Returns Success(None)
verifier1.performOneOperation(op2) // Returns Success(None)
verifier1.performOneOperation(op3) // Returns Success(None)
verifier1.digest match {
case Some(d1) if d1.sameElements(digest1) =>
//If digest1 from the prover is already trusted, then verification of the second batch can simply start here
val verifier2 = new BatchAVLVerifier[Digest32, Blake2b256.type](d1, proof2, keyLength = 1, valueLengthOpt = Some(8), maxNumOperations = Some(3), maxDeletes = Some(1))
verifier2.performOneOperation(op4) // Returns Success(Some(Longs.toByteArray(10)))
verifier2.performOneOperation(op6) // Returns Success(Some(Longs.toByteArray(30)))
verifier2.performOneOperation(op8) // Returns Success(Some(Longs.toByteArray(30)))
verifier2.digest match {
case Some(d2) if d2.sameElements(digest2) => println("first and second digest value and proofs are valid")
case _ => println("second proof or announced digest NOT valid")
}
case _ =>
println("first proof or announced digest NOT valid")
}
[TODO: describe MerkleTree & MerkleProof classes]
Run sbt test
from a folder containing the framework to launch tests.
Run sbt bench:test
from a folder containing the framework to launch embedded benchmarks.
The code is under Public Domain CC0 license means you can do anything with it. Full license text is in COPYING file
Clone the repository
$git clone <repository> scrypto
$cd scrypto
The code uses Scalablytyped to generate Scala.js bindings
for @noble/hashes
. This video explains how the
environment for ScalablyTyped is configured in this repository.
Before compiling the library with SBT, you need to install JS dependencies for ScalablyTyped.
The configuration is in package.json
.
$npm install
added 285 packages, and audited 286 packages in 20s
found 0 vulnerabilities
Then you can compile the library with SBT and run tests. Please note that ScalaJS compilation of JavaScript code
may consume a lot of memory, so makes sense to give more memory to SBT by e.g. running
export SBT_OPTS="Xmx3G XX:+UseConcMarkSweepGC XX:+CMSClassUnloadingEnabled XX:MaxPermSize=2G Xss2M"
(to give 3G
of RAM to it) before launching SBT.
$sbt
sbt:scrypto> compile
sbt:scrypto> test
Your contributions are always welcome! Please submit a pull request or create an issue to add a new cryptographic primitives or better implementations.