^{Special thanks my followers for supporting me:}

VDF4J A Verifiable Delay Function (VDF) is a function that requires substantial time to evaluate (even with a polynomial number of parallel processors) but can be very quickly verified as correct. VDFs can be used to construct randomness beacons with multiple applications in a blockchain network environment.

^{Visit warp.dev to learn more.}

---

VDF4J

Table of Contents

1. Introduction
2. What Is a Verifiable Delay Function (VDF)?
3. Getting started
4. Java Example Code
5. Project Goals
6. Directory Structure
7. Build & Run Instructions
8. Building Native Library
   • Linux Concept
   • Windows Concept
9. How It Works
   • Core Concept
   • Computation Delay
   • Proof Generation & Verification
10. Limitations & Future Work
11. .Legal Warning
12. Support
13. Note
14. Contribute
15. Authors
16. License

---

Introduction

VDF4J is a Java-based Verifiable Delay Function (VDF) implementation, developed primarily for educational and research purposes for Adrestus cryptocurrency project. Part of the code is forked from previous libraries that failed to provide consistent and robust functionality, resulting in memory leaks and significant issues on the JVM. VDFs require a predetermined amount of sequential time to compute yet are very fast to verify, making them attractive for use cases like blockchain consensus, randomness beacons, and anti-front-running measures.

---

What Is a Verifiable Delay Function (VDF)?

This VDF implementation is written in Java. The GMP library is used for arithmetic and greatest common divisor (GCD) calculations. We use class groups to implement the approaches described in the following papers:

1. Simple Verifiable Delay Functions Pietrzak, 2018.
2. Simple Efficient Verifiable Delay Functions Wesolowski, 2018.

A Verifiable Delay Function (VDF) is a function that:

1. Takes a certain minimum amount of time to compute (no matter how many parallel processors you have, it cannot be computed faster than some specified minimum time).
2. Generates a proof that the output is correct.
3. Allows very rapid verification of that proof, much faster than the time taken to compute the original function.

In practice, VDFs use repeated or iterative cryptographic operations (often exponentiation in a group with specific properties). The verification step uses cryptographic checks that confirm the original computation was done correctly. By introducing a time delay during evaluation, VDFs prevent malicious actors from influencing output. The output cannot be differentiated from a random number until the final result is computed. See https://eprint.iacr.org/2018/712.pdf for more details.

---

Getting started

The latest release is: 0.1.1.

It is available in Maven Central as: com.security.VDF4J:VDF4J:0.1.1:

JDK 21 is required to build and run this project.

<dependency>
    <groupId>io.github.l16h7n1n6s</groupId>
    <artifactId>VDF4J</artifactId>
    <version>0.1.1</version>
</dependency>

Java Example Code

public class ExamplesTest {
    private static byte[] challenge;
    private static long difficulty;
    private static Random random;

    @BeforeAll
    public static void Setup() {
        challenge = new byte[20];
        difficulty = 100;
        random = new Random();
        random.setSeed(200);
    }

    @Test
    public void test_vdf_Pietrzak() throws NoSuchAlgorithmException {
        random.nextBytes(challenge);
        VdfEngine vdf = new VdfEnginePietrzak(2048);
        byte[] solution = vdf.solve(challenge, difficulty);
        assertEquals("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", Hex.toHexString(solution));
        assertTrue(vdf.verify(challenge, difficulty, solution));
        vdf.cleanup();
    }
}

Project Goals

1. Illustrate VDF Principles: Help developers and researchers understand the mechanics behind a verifiable delay function.
2. Educational Resource: Provide a clear, well-structured Java codebase as a learning tool.
3. Modular Implementation: Lay the groundwork so contributors can improve or adapt VDF logic for different use cases (blockchains, cryptographic protocols, etc.).

Important: This implementation is intended for demonstration. It may not have production-grade security optimizations.

---

Directory Structure

• src/main/java/
  Contains the core Java source code implementing the VDF logic:

  • VDF function (sequential computation logic)
  • Proof generation/verification modules
  • Testing/demo classes

• pom.xml
  The Maven configuration file that defines build dependencies and plugins.

• appveyor.yml
  Potential integration/build instructions for AppVeyor continuous integration (not always used in Java projects, but can be adapted).

• dependency-reduced-pom.xml
  An auxiliary Maven file generated during shading/assembly processes.

• LICENSE
  MIT License details.

• README.md
  You’re reading it!

---

Build & Run Instructions

Prerequisites

• Java 8 or higher
• Maven (3.x or newer) or any other environment that can handle a Maven project

Step-by-Step

1. Clone the Repository:

   git clone https://github.com/PanagiotisDrakatos/VDF4J.git
   cd VDF4J

2. Build via Maven: This will compile the project and run any included tests.

   mvn clean install

3. Run Demo Example (If Provided):

   mvn -Dtest=MyTest ExamplesTest test

Replace com.example.vdf.Main with the actual main class path from the repository. This example shows how you might launch a test or demonstration of the VDF functionality.

Building Native Library

Below are instructions for building the libgmp library that ships with this module

1.0 Linux

There is an included Dockerfile that can be used to compile libgmp with

First build the Docker image. Navigate to the nativeCode folder of the project and run the following command:

docker build -t jnagmp-linux-x86-64 -f linux-x86-64.dockerfile .

Next run the Docker image which will execute the Makefile and output the compiled library in to src/main/resources

docker run -v "$(pwd)/src/main/resources:/build/src/main/resources" -t jnagmp-linux-x86-64

After running the above command, the compiled library will be available in the resources folder.

2.0 Windows

1 Option: Using cygwin

Prerequisites

• Operating System: Windows 7 or later (32-bit or 64-bit).
• Internet Connection: Required to download the installer and select packages.
• Administrative Privileges: Recommended for easier installation and future package management.

---

Download Cygwin

1. Navigate to the official Cygwin website.
2. Choose the appropriate installer:
   • setup-x86_64.exe for 64-bit Windows
   • setup-x86.exe for 32-bit Windows

Tip: If you’re unsure of your system architecture, press Windows Key + Pause/Break to open system properties and check your system type.

---

Run the Installer

1. Double-click the downloaded setup-x86_64.exe (or setup-x86.exe for 32-bit).
2. If prompted by User Account Control (UAC), click Yes to allow changes.
3. Choose “Install from Internet” (typical usage) when asked “Choose a Download Source.”

---

Select Packages to install from Cygwin

1. i686-w64-mingw32-gcc: For 32-bit Windows applications. Not exist on packet manger run version command to check it on cygwin

2. x86_64-w64-mingw32-gcc: For 64-bit Windows applications. Not exist on packet manger run version command to check it on cygwin

3. mingw64-i686-gcc-g++ - The GNU Compiler Collection (C++ compiler). For 32-bit Windows applications.

4. mingw64-x86_64-gcc-g++ - The GNU Compiler Collection (C++ compiler). For 64-bit Windows applications.

5. install cygwin on setup make sure all this installed

6. gcc-core - The GNU Compiler Collection (C compiler).

7. gcc-g++ - The GNU Compiler Collection (C compiler).

8. libgcc - The GNU Compiler Collection (C compiler).

9. make - The GNU version of the 'make' utility.

10. autoconf - A tool for automatically configuring source code.

11. automake - A tool for automatically generating Makefile.in files.

12. libtool - A generic library support script.

13. m4 - A macro processing language.

14. curl - A tool to transfer data from or to a server.

Add the following to your PATH environment variable

C:\cygwin64\bin

Make sure you have the following run:

i686-w64-mingw32-gcc --version For 32-bit Windows applications
x86_64-w64-mingw32-gcc --version For 64-bit Windows applications

Navigate to the directory where you want to install run commands

cd /cygdrive/c/Users/User/mypath...

Runs the following command to compile the library

 cd gmp-6.3.0

Run the following command to compile the library

./configure --disable-static --enable-shared --host=x86_64-w64-mingw32

Or better run this command to compile the library from a ready to use Makefile fro windows

 make -f WindowsMakefile.libgmp install

Then run the following command to move the files into the resources folder that the program reads

mv libgmp-10.dll libgmp.dll

How It Works

Core Concept

1. Input / Setup:

   • A challenge input (typically an integer, group element, or random data) is prepared.
   • System parameters (e.g., group modulus for repeated squaring, number of iterations, etc.) are established.

2. Iterated Computation:

   • The VDF function in this repository likely uses an iterative exponentiation or repeated squaring approach— repeated many times to enforce a real-world time delay.
   • Due to the nature of exponentiation in large number fields, parallelization beyond a certain point doesn’t help reduce the overall sequential time.

3. Output + Proof:

   • After the heavy sequential computation, the function returns a result plus a cryptographic proof of correctness (often involving repeated squaring proofs or isogenies, depending on the chosen VDF approach). Computation Delay

Computation Delay

• The key to a VDF is that computing the output from the input must take at least t steps (each step depends on the previous step’s result).

• Even with many parallel processors, you cannot skip or batch these steps because each iteration relies on the output of the previous iteration. Proof Generation & Verification

Computation Delay

• Proof: Typically involves additional math that allows an observer to quickly confirm that you actually performed all t steps.
• Verification: By using cryptographic checks (like checking an exponent or some tricky modular arithmetic property), the proof is validated in log(t) or similarly small complexity.

Limitations & Future Work

1. Security Hardening: For real-world usage, consider larger parameters or proven secure group settings (e.g., class groups, pairing-friendly curves, incremental exponentiation in large prime fields, etc.).

2. Performance Tuning: The Java code works for a demonstration; you may want advanced libraries or native bindings for enhanced big-integer arithmetic.

3. Expand Proof Options: Different VDF constructions exist (e.g., Wesolowski’s proof, Pietrzak’s proof). This project could implement or compare multiple proof schemes.

4. Benchmarking: Adding thorough benchmarks to measure performance under various iteration counts, machine architectures, or concurrency scenarios would be beneficial.

5. Integration with Other Projects: Useful for demonstrating real ownership or usage in blockchains or distributed systems needing delayed computations

Legal Warning

While this may be helpful for some, there are significant risks. VDF4J may be used only for Educational Purposes. Do not use it for illegal purposes! You could go to jail if if you will use it for malicious purposes.

Support

For support, email [email protected] or join me Discord:panos5427. Meaning, if you liked using this app or it has helped you in any way, I'd like you send me an email about anything you'd want to say about this software. I'd really appreciate it!

Note

• ⭐️ Give me a Star!! VDF4J is constantly updating, support us!
• The analysis was done by me, without having obfuscated the source code (either with pyarmor etc),
• I would not recommend using VDF4J + obfuscatebecause many times av trigger obfuscated codes as false positive even if legitimate.

Contribute

1. Fork it: git clone https://github.com/PanagiotisDrakatos/VDF4J.git
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request

Authors

• @panagiotisdrakatos

License

This project is distributed under the MIT license version 2.0 (see the LICENSE file in the project root).

By submitting a pull request to this project, you agree to license your contribution under the MIT license version 2.0 to this project.