Welcome to OpenPrivML, a collaborative ecosystem for secure and efficient machine learning. This repository contains core modules, documentation, and examples aimed at helping researchers, developers, and practitioners build ML workflows that protect data confidentiality through advanced cryptographic techniques.

• Overview
• Key Features
• Architecture
• Installation
• Usage Examples
• Repository Structure
• Contributing
• Community & Governance
• License
• Citing OpenPrivML
• Acknowledgments
• Contact

OpenPrivML is a project to develop a robust open-source ecosystem for privacy-preserving ML. Our goal is to reconcile the tension between high-security requirements and the performance demands of modern deep learning pipelines. Through a blend of homomorphic encryption, secure multiparty computation, and targeted model compression and pipelining strategies, we aim to enable near real-time processing of confidential data across healthcare, finance, and other sensitive domains.

1. Hongyi Wu – University of Arizona (PI)
2. Rui Ning – Old Dominion University

• Homomorphic Encryption: Allows computations directly on encrypted data for secure inference.
• Secure Multiparty Computation: Distributes computation among multiple parties to maintain privacy.
• Model Compression & Optimization: Uses advanced pruning, layer consolidation, and pipelining to reduce latency.
• Modular Design: Easy integration with popular ML libraries (e.g., TensorFlow, PyTorch) and cryptographic backends.
• Community-Driven: Encourages external contributions, domain-specific optimizations, and transparent governance.

OpenPrivML adopts a layered architecture:

1. Core Crypto Layer: Implements homomorphic encryption, secure MPC, and other cryptographic primitives.
2. ML Integration Layer: Bridges between standard ML frameworks and our crypto layer, handling encryption/decryption workflows.
3. Optimization Layer: Provides compression, pipelining, and caching strategies for efficient computation on resource-limited devices.
4. Application Layer: Contains example applications, demos, and domain-specific integrations (e.g., healthcare, finance).

Prerequisites:

• Python 3.8+
• Git
• Recommended: virtual environment (e.g., venv, Conda)

Steps:

# Clone the repository
git clone https://github.com/your-org/openprivml.git
cd openprivml

# (Optional) Create and activate a virtual environment
python -m venv venv
source venv/bin/activate   # or venv\Scripts\activate on Windows

# Install dependencies
pip install -r requirements.txt

# Verify successful installation
pytest tests