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📡 ZkWave: Transforming Voice into Secure Proofs 🎤🔐

🚀 Overview

ZkWave is an innovative project that combines audio signal processing, polynomial mathematics, and zero-knowledge cryptography. With ZkWave, we securely convert voice data into mathematical polynomials and leverage Zero-Knowledge Proofs (ZKP) to verify voice-based information without exposing sensitive data.

💡 Features

  • 🎧 Voice-to-Polynomial Transformation
    Transform audio signals into polynomials for efficient compression, encryption, and secure representation.

  • 🔒 Privacy-Preserving Voice Authentication
    Authenticate users through voice securely using zero-knowledge cryptography.

  • 🛡️ Zero-Knowledge Encryption
    Enable secure and private proof generation using zk-SNARKs or zk-STARKs.

📚 How It Works

  1. Voice Signal Processing

    • Record and digitize voice input (WAV format).
    • Preprocess the audio for noise reduction and normalization.

  2. Polynomial Representation

    • Model voice signals as mathematical polynomials using least squares approximation or spline fitting.
    • Compress voice features into polynomial coefficients.

  3. Zero-Knowledge Proofs

    • Generate ZKPs for the polynomial representation using Circom and snarkjs.
    • Verify proofs without revealing the underlying voice data.

🛠️ Tech Stack

  • Programming Language: Rust 🦀
  • Cryptographic Framework: Circom, snarkjs 🔐
  • Audio Processing: Rust's wav and ndarray crates
  • Blockchain Integration (Optional): zkSync for decentralized verification

🌐 Real-World Use Cases

  1. Voice Authentication
    Verify users’ identities through private voice-based authentication.

  2. Secure Communication
    Enable provably secure and private voice transmissions.

  3. Privacy-Preserving Voice Analytics
    Analyze voice features without exposing sensitive data.

📋 Project Completion Checklist

1. Voice Processing Implementation

  • Write Rust code to load and process WAV audio files.
  • Implement a function to extract audio samples from the WAV file.
  • Normalize and fit the audio samples to a polynomial using regression.
  • Serialize the polynomial coefficients and evaluation data into a JSON input format for Circom.

2. Circom Circuit Development

  • Design a Circom circuit for verifying the polynomial evaluation (VerifyPolynomial).
  • Add debug signals to the circuit for easier debugging and validation.
  • Compile the Circom circuit into r1cs, wasm, and other required formats.
  • Test the circuit using valid JSON inputs to ensure correctness.
  • Optimize the circuit for performance and size.

3. Zero-Knowledge Proof Integration

  • Set up the Groth16 proving system using snarkjs.
  • Generate the trusted setup phase (.zkey file) using a precomputed PTAU.
  • Create the proof and verify it locally with snarkjs.
  • Export the proof and verification key for use in the mini app.

4. Private TON Mini App

  • Design and implement the private TON mini app interface.
  • Integrate the zero-knowledge proof verification in the mini app.
  • Implement secure storage and handling for sensitive user data.

🚀 Future Enhancements (Optional)

  • Extend support for additional audio file formats (e.g., MP3, FLAC).
  • Implement more efficient polynomial fitting algorithms.
  • Explore alternative zero-knowledge proof systems for optimization.

🤝 Contributing

We welcome contributions! Whether it’s reporting bugs, suggesting features, or submitting pull requests, check out our Contributing Guide.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🎉 Join the Wave!

Be part of the privacy revolution with ZkWave. If you have ideas, feedback, or want to collaborate, feel free to open an issue or contact us!