dplab 0.0.2

DPLab: Benchmarking Differential Privacy Aggregation Operations

DPLab: Benchmarking Differential Privacy Aggregation Operations

This repo targets to provide a unified interface to access and evaluate the same aggregation functionalities in different open-source differential privacy (DP) libraries. With a simple CLI, one can choose the library, the aggregation function, and many other experimental parameters and apply the specified DP measurement to data stored in a .csv file. The repo also provides both synthetic and real-world example datasets for evaluation purposes. Evaluation results are stored in a .json file and metrics are provided for repeated experiments. The repo also provides a CLI tool to generate configuration groups for larger-scale comparison experiments.

Currently supported aggregation operations:

• COUNT
• SUM
• MEAN
• VAR
• MEDIAN
• QUANTILE

Currently supported libraries:

• diffprivlib 0.5.2 [Homepage] [Example Usage]
• python-dp 1.1.1 [Homepage] [Example Usage]
• opendp 0.5.0 [Homepage] [Example Usage]
• tmlt.analytics 0.4.1 [Homepage] [Example Usage]
• chorus [Homepage] [Example Usage]

Installation

Clone the repo, switch the working directory, and install the dependencies

git clone [e-mail]:camelop/dp_lab.git
cd dp_lab
pip install -e .

To use tmlt

export PYSPARK_PYTHON=/usr/bin/python3
sudo apt install openjdk-8-jre-headless
pip3 install -i https://d3p0voevd56kj6.cloudfront.net python-flint
pip3 install tmlt.analytics

To use chorus, please make sure you have Java runtime installed.

How to run experiments in the benchmark

Generate the experiment commands, this will generate an ./exp.db.json file under the working directory (you can also use --location to specify a different place).

dplab_exp plan --repeat 100 --group_num 100

Queue the experiments for execution

dplab_exp launch --debug

How to run dp libraries in the benchmark

Run a specific library with the CLI

dplab_run <library> <operation> <input_file> <output_file> <other options>

For example:

dplab_run pydp sum data/1.csv data/1.json -f -r 1000

Other options include:

• mode: Evaluation mode, one can choose from "plain" (no timing/mem measurement), "internal" (internal measurement), or "external" (external tracking).
• epsilon: DP parameter.
• quant: Quantile value for QUANTILE operation, a float number between 0 and 1.
• repeat: How many time should the evaluation repeat.
• force: Force to overwrite the output file.
• debug: Include debugging information in the output file.
• python_command: Python command used to run the script in the external mode.
• external_sample_interval: timing/mem consumption sample interval in the external mode.

For more information, please check the main entry file.

Generating synthetic data

# Make sure you are in the root directory of the repo
# Data will be generated in the ./data/ directory
# The procedure will generate about 28GB of data
# To avoid the risk of running out of disk space, you can comment out the performance test lines (Line26-27) in SYN_TARGETS defined in the script
python3 scripts/gen_data.py