The goal of this project is to understand (and optimize) the system-level bottlenecks of private inference protocols under varying workloads. This repo contains the codes for our paper. The high-level directories are:

1. garbled_circuits: contains the raw data for benchmarking ReLU Garbling and Evaluation on the embedded device and the server
2. layer_parallel_HE: contains our code and the raw data for enabling layer-parallel homomorophic evaluation of linear layers
3. simulator: our PI simulator used to generate plots and results from the paper
4. artifact : contains scripts to generate key figures in the paper

Please refer to the READMEs within each subdirectory to learn how we collected data and how to run the simulator.

First, clone this repo:

git clone https://github.com/kvgarimella/characterizing-private-inference.git

Then, change directories and install the Python dependencies:

cd  characterizing-private-inference
pip install -r requirements.txt

As a simple test, navigate to the simulator/experiments sub-directory and run:

cd simulator/experiments
python simulate_server_garbler.py

This will run a single simulation of the Server-Garbler protocol and store the results in a directory called tmp.

Please refer to the README within the artifact directory.

If you find our work useful, please use the following citation:

@inproceedings{10.1145/3582016.3582065,
author = {Garimella, Karthik and Ghodsi, Zahra and Jha, Nandan Kumar and Garg, Siddharth and Reagen, Brandon},
title = {Characterizing and Optimizing End-to-End Systems for Private Inference},
year = {2023},
publisher = {Association for Computing Machinery}, address = {New York, NY, USA},
url = {https://doi.org/10.1145/3582016.3582065}, doi = {10.1145/3582016.3582065},
booktitle = {Proceedings of the 28th ACM International Conference on
Architectural Support for Programming Languages and Operating Systems, Volume 3},
series = {ASPLOS 2023}}

• Karthik Garimella - Python, Rust, C++
• Zahra Ghodsi - Rust, C++