VoiceKey

Privacy Preservation with Zero-Knowledge Proofs (ZKP) and Blockchain

Table of Contents

  1. Introduction
  2. The Need for Privacy in Pre-Selection MFA
  3. Zero-Knowledge Proofs (ZKP) in Protecting User Identity
  4. Blockchain for Immutable and Private Attestation
  5. Analog Voice Verification Near the KeyVoice Analyzer
  6. Integration of ZKP and Blockchain in VoiceKey
  7. Implementation Details
  8. Benefits and Challenges
  9. Conclusion
  10. References
  11. Contact Information
  12. Acknowledgments

Introduction

In the VoiceKey system, privacy preservation is paramount, especially during the pre-selection Multi-Factor Authentication (MFA) phase and the handling of sensitive user identification information. This document explores how Zero-Knowledge Proofs (ZKPs) and blockchain technology are employed to protect the privacy of MFA data and user identities. Additionally, it discusses the necessity of analog voice verification near the KeyVoice analyzer to overcome the limitations of digital recordings.

The Need for Privacy in Pre-Selection MFA

Zero-Knowledge Proofs (ZKP) in Protecting User Identity

Understanding ZKPs

A Zero-Knowledge Proof allows one party (the prover) to prove to another (the verifier) that they know a value ( x ) without revealing any information about ( x ) itself. This ensures that sensitive information remains confidential during the authentication process.

Key Properties:

  1. Completeness: If the statement is true, the verifier will be convinced.
  2. Soundness: If the statement is false, no dishonest prover can convince the verifier.
  3. Zero-Knowledge: No additional information is revealed beyond the validity of the statement.

Applying ZKPs to MFA Data

Blockchain for Immutable and Private Attestation

Role of Blockchain in Privacy Preservation

Storing ZKPs on a Layer 2 EVM

Analog Voice Verification Near the KeyVoice Analyzer

Limitations of Digital Voice Records

Ensuring Authenticity with Analog Signals

Integration of ZKP and Blockchain in VoiceKey

System Architecture

Components:

  1. User Device: Handles pre-selection MFA and generates ZKPs for MFA data and identification information.
  2. Blockchain Network: Stores ZKPs and attestation proofs securely.
  3. Verification Nodes: Verify ZKPs without accessing the underlying sensitive data.
  4. KeyVoice Analyzer: An analog device that performs the advanced voice authentication locally.

Architecture Diagram:

(Include a diagram showing the flow from user device to blockchain and then to the analog KeyVoice analyzer.)

Authentication Workflow

  1. Pre-Selection MFA on User Device:
    • User completes MFA steps (e.g., password entry, biometric scan).
    • User’s identification information is used for authentication.
  2. ZKP Generation for MFA Data:
    • User device generates ZKPs proving possession of correct MFA credentials and identity without revealing them.
    • ZKPs are signed with the user’s private key associated with their blockchain wallet.
  3. Submission to Blockchain:
    • ZKPs are submitted to the blockchain.
    • A transaction records the proof without exposing sensitive data.
  4. Verification of ZKPs:
    • Verification nodes execute smart contracts to validate ZKPs.
    • Successful verification updates the user’s authentication status on the blockchain.
  5. Analog Voice Verification:
    • User proceeds to the KeyVoice analyzer in person.
    • Voice authentication is performed using analog signals, capturing infinite depth and nuances.
  6. Final Authentication Outcome:
    • If analog voice verification is successful, access is granted.
    • The system ensures that only users who have passed both the pre-selection MFA and analog voice authentication are authenticated.

Implementation Details

ZKP Protocols for MFA Data

Choice of Protocol: Non-Interactive Zero-Knowledge Proofs (NIZKs) for efficient, one-way proof submission.

Protocol Steps:

  1. Prover (User Device):
    • Encodes MFA data and identification information into a mathematical statement.
    • Generates a ZKP that they know the inputs satisfying the statement without revealing them.
  2. Verifier (Blockchain Smart Contract):
    • Validates the proof using public parameters.
    • Does not access or learn any sensitive information.

Blockchain Layer for Secure Attestation

Code Examples

Generating a ZKP for MFA Data (Simplified Pseudocode)

# Import ZKP library
from zkp_library import NIZKProver, TrustedSetup

# Trusted setup (one-time process)
public_params, private_params = TrustedSetup.generate_params()

def generate_mfa_proof(mfa_data, identification_info, public_params):
    # Combine MFA data and identification information
    secret_data = hash(mfa_data + identification_info)
    
    # Generate proof
    proof = NIZKProver.generate_proof(secret_data, public_params)
    return proof

# Example usage
mfa_data = get_mfa_data()  # User's MFA inputs
identification_info = get_identification_info()  # User's personal info

proof = generate_mfa_proof(mfa_data, identification_info, public_params)

Smart Contract for ZKP Verification (Solidity Example)

pragma solidity ^0.8.0;

contract MFAZKPVerifier {
    event AuthenticationSuccessful(address indexed user);

    function verifyMFAProof(
        bytes memory proof,
        bytes32 publicInputHash
    ) public returns (bool) {
        // Verification logic using ZKP library
        bool isValid = NIZKVerifier.verify(proof, publicInputHash);

        if (isValid) {
            emit AuthenticationSuccessful(msg.sender);
            // Update user's authentication status
        }

        return isValid;
    }
}

Explanation:

Benefits and Challenges

Benefits

Challenges

Conclusion

By integrating Zero-Knowledge Proofs and blockchain technology into the pre-selection MFA phase, the VoiceKey system ensures that sensitive user data and identification information remain private and secure. The use of ZKPs allows users to prove their identity and MFA credentials without revealing any underlying data, while the blockchain provides an immutable ledger for attestation.

The requirement for analog voice verification near the KeyVoice analyzer addresses the limitations of digital recordings, capturing the infinite depth and nuances of the human voice that are critical for robust authentication. This approach significantly enhances security by preventing digital spoofing and ensuring the authenticity of the voice signal.

The combined use of these technologies aligns with the AI Integrity Alliance’s commitment to advancing secure, privacy-preserving authentication methods that leverage innovative solutions to meet emerging security challenges.

References

  1. Ben-Sasson, E., et al. (2014). Zerocash: Decentralized Anonymous Payments from Bitcoin. IEEE Symposium on Security and Privacy, 459-474.
  2. Goldreich, O., Micali, S., & Wigderson, A. (1991). Proofs that Yield Nothing But Their Validity. Journal of the ACM, 38(3), 691-729.
  3. Chaum, D., & Pedersen, T. P. (1992). Wallet Databases with Observers. CRYPTO ‘92, 89-105.
  4. Boneh, D., & Shoup, V. (2020). A Graduate Course in Applied Cryptography. Online Book.
  5. Buterin, V. (2014). Ethereum White Paper: A Next-Generation Smart Contract and Decentralized Application Platform. Ethereum Foundation.
  6. Sasson, E. B., et al. (2014). SNARKs for C: Verifying Program Executions Succinctly and in Zero Knowledge. CRYPTO 2013, 90-108.
  7. Menezes, A. J., Van Oorschot, P. C., & Vanstone, S. A. (1996). Handbook of Applied Cryptography. CRC Press.

Contact Information

AI Integrity Alliance

Acknowledgments

We extend our gratitude to the cryptography, blockchain, and cybersecurity communities for their foundational work in privacy-preserving technologies. Their research and innovations have been instrumental in developing advanced authentication systems like VoiceKey that prioritize user privacy and security.

Note: This document is part of the VoiceKey project by the AI Integrity Alliance. It provides a detailed exploration of how Zero-Knowledge Proofs and blockchain technology are utilized to enhance privacy and security in the pre-selection MFA phase and protect user identification information. The document also emphasizes the importance of analog voice verification to capture the full depth of human voice characteristics essential for robust authentication.

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