👻 Ghost Protocol

Privacy-Preserving AI Training on Decentralized Healthcare Data

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🛑 The Truth About Privacy AI

Most collaborative learning demos are fake. They simulate distributed networks on a single CSV file and call it a day.

Ghost Protocol is different. This is a true Federated Learning (FL) system designed for the hostile reality of healthcare data privacy (DPDP/GDPR). It allows hospitals to collaboratively train lifesaving AI models without a single byte of patient data ever leaving their premises.

"We don't move the data to the model. We move the model to the data."

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🛠️ Key Innovations

1. 🔒 Real Homomorphic Encryption (NO SIMULATIONS)

We rely on Paillier Encryption (via phe library) to encrypt model gradients.

• The "Secure National Aggregator" performs Homomorphic Addition on encrypted ciphertexts.
• The server never sees the raw updates, only the mathematical sum.
• Proof: Check run_secure_protocol.py for the implementation.

2. 🧠 Differential Privacy (Opacus)

We don't just "hope" for privacy; we calculate it.

• Strict implementation of Opacus (PyTorch) to inject Gaussian noise.
• Guaranteed (ε, δ)-Differential Privacy budget tracking.
• Prevents model inversion attacks (reconstructing patient faces/data from weights).

3. 🛡️ Byzantine Shield

Distributed systems are vulnerable to "Poisoning Attacks" (malicious nodes).

• Our Shapley Value analysis automatically detects and quarantines nodes that contribute harmful gradients.

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🚀 Quick Start

Prerequisites

• Python 3.9+
• Redis (for message queue)

1. Clone & Install

git clone https://github.com/yourusername/ghost-protocol.git
cd ghost-protocol
pip install -r requirements.txt

2. Run the "Real Math" Demo

Witness the full cryptographic cycle (Encryption -> Aggregation -> Decryption) on your local machine.

python run_secure_protocol.py

warning: This script performs real 1024-bit encryption. It is computationally intensive.

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🏗️ Architecture

flowchart TD
    %% Nodes
    User([🚀 Start Training])
    
    subgraph Cloud ["☁️ Untrusted Information Exchange (The Internet)"]
        SNA["🏢 Secure National Aggregator <br/> (Violates Privacy if Data leaks)"]
        GlobalModel{"🧠 Global Model State"}
    end

    subgraph HospitalA ["🏥 Appollo Hospital (Secure Enclave)"]
        DataA[("📂 Patient Data <br/> (Never Leaves)")]
        LocalModelA["⚙️ Local Model"]
        NoiseA["🎲 Opacus Engine <br/> (Differential Privacy)"]
        EncryptA["🔒 Paillier Encryption <br/> (Homomorphic)"]
    end

    subgraph HospitalB ["🏥 AIIMS Hospital (Secure Enclave)"]
        DataB[("📂 Patient Data <br/> (Never Leaves)")]
        LocalModelB["⚙️ Local Model"]
        NoiseB["🎲 Opacus Engine <br/>(Differential Privacy)"]
        EncryptB["🔒 Paillier Encryption <br/> (Homomorphic)"]
    end

    %% Logic Flow
    User -->|1. Initialize| SNA
    SNA -->|2. Broadcast Logic| LocalModelA
    SNA -->|2. Broadcast Logic| LocalModelB

    DataA -->|3. Train| LocalModelA
    LocalModelA -->|4. Add Noise| NoiseA
    NoiseA -->|5. Encrypt Weights| EncryptA
    
    DataB -->|3. Train| LocalModelB
    LocalModelB -->|4. Add Noise| NoiseB
    NoiseB -->|5. Encrypt Weights| EncryptB

    EncryptA -->|6. Send Ciphertext| SNA
    EncryptB -->|6. Send Ciphertext| SNA

    SNA -->|7. Blind Aggregation| GlobalModel
    GlobalModel -.->|8. Distribute New Intelligence| LocalModelA
    GlobalModel -.->|8. Distribute New Intelligence| LocalModelB
    
    %% Styling
    style Cloud fill:#f9f9f9,stroke:#333,stroke-width:2px,stroke-dasharray: 5 5
    style HospitalA fill:#e1f5fe,stroke:#01579b
    style HospitalB fill:#e1f5fe,stroke:#01579b
    style SNA fill:#ffcdd2,stroke:#b71c1c
    style DataA fill:#fff9c4,stroke:#fbc02d
    style DataB fill:#fff9c4,stroke:#fbc02d

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🤝 Contributing & Security

Privacy is a moving target. While we rely on mathematically proven libraries (opacus, phe), implementation bugs are always possible.

We believe in "Security through Visibility", not obscurity.

• Found a bug? Please open an Issue. We treat security reports with highest priority.
• Want to break it? We invite cryptographers and engineers to audit the run_secure_protocol.py implementation.
• Pull Requests: Welcome! Help us optimize the Paillier encryption steps or different model architectures.

We are building this with the community, not just for it.

📜 License

This project is open-sourced under the MIT License. Simulations are easy. Privacy is hard. We chose the hard way.