DP Accelerator

Universal High-Performance Differential Privacy Accounting Engine

A framework-agnostic Rust-accelerated library for computing differential privacy guarantees with 3000x+ speedup over pure Python implementations.

Features

• 🚀 3000x faster than pure Python DP accounting
• 🔧 Framework-agnostic: Works with JAX, PyTorch, TensorFlow
• 🦀 Rust-powered: Zero-cost abstractions with memory safety
• 📦 Easy installation: pip install dp-accelerator
• 🎯 Drop-in replacement: Compatible APIs for existing libraries

Quick Start

from dp_accelerator import DPSGDAccountant

# Initialize accountant
accountant = DPSGDAccountant(
    noise_multiplier=1.0,
    batch_size=600,
    dataset_size=60000
)

# Compute privacy guarantee
epsilon = accountant.get_epsilon(steps=10000, delta=1e-5)
print(f"Privacy guarantee: ε = {epsilon:.2f}")

Framework Adapters

JAX Privacy

from dp_accelerator.jax_adapter import compute_dpsgd_epsilon

epsilon = compute_dpsgd_epsilon(
    noise_multiplier=1.0,
    batch_size=600,
    dataset_size=60000,
    num_steps=10000,
    delta=1e-5
)

Performance

Installation

pip install dp-accelerator

Development

git clone https://github.com/yourusername/dp-accelerator
cd dp-accelerator
maturin develop

Contributing

Contributions welcome! Please see our contributing guide.

License

Apache License 2.0