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dplab-0.0.7
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DPLab: Benchmarking Differential Privacy Aggregation Operations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dplab-0.0.7 |
| نام | dplab |
| نسخه کتابخانه | 0.0.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | littleRound |
| ایمیل نویسنده | xiaoyuanliu@berkeley.edu |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/dplab/ |
| مجوز | - |
# 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.
## 1-Min Tutorial
Get hands-on in 1 minute with [**our tutorial notebook**](https://colab.research.google.com/drive/1jtsiCW-pQwOlIlQHpKgN_PbSjpReiTIw?usp=sharing).
**Currently supported aggregation operations**:
- COUNT
- SUM
- MEAN
- VAR
- MEDIAN
- QUANTILE
**Currently supported libraries**:
- diffprivlib 0.5.2 [[Homepage](https://github.com/IBM/differential-privacy-library)] [[Example Usage](./src/dplab/library_workload/diffprivlib.py)]
- python-dp 1.1.1 [[Homepage](https://github.com/OpenMined/PyDP)] [[Example Usage](./src/dplab/library_workload/pydp.py)]
- opendp 0.6.1 [[Homepage](https://opendp.org/)] [[Example Usage](./src/dplab/library_workload/opendp.py)]
- tmlt.analytics 0.4.1 [[Homepage](https://docs.tmlt.dev/analytics/latest/index.html)] [[Example Usage](./src/dplab/library_workload/tmlt.py)]
- chorus 0.1.3 [[Homepage](https://github.com/uvm-plaid/chorus)] [[Example Usage](./src/dplab/library_workload/chorus.py)]
## Installation
To install dplab, one can **use the package on pypi**
```sh
pip install dplab
```
Or **with source code**: clone the repo, switch the working directory, and install the dependencies
```sh
git clone git@github.com:camelop/dp_lab.git
cd dp-lab
pip install -e .
```
To use [tmlt](https://docs.tmlt.dev/analytics/latest/installation.html)
```sh
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](https://github.com/uvm-plaid/chorus), please make sure you have Java runtime installed.
(If you have already installed tmlt, it should be fine.)
## How to run dp libraries in the benchmark
Run a specific library with the CLI
```sh
dplab_run <library> <operation> <input_file> <output_file> <other options>
```
For example:
```sh
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, default is set to `1`.
- `quant`: Quantile value for QUANTILE operation, a float number between 0 and 1.
- `lb`: The optional value lower bound estimation used when applying certain differential privacy aggregations.
- `ub`: The optional value upper bound estimation used when applying certain differential privacy aggregations.
- `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](./src/dplab/main.py).
### Generating synthetic data
```sh
# 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
```
## 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).
```sh
dplab_exp plan --repeat 100 --group_num 100
```
Queue the experiments for execution
```sh
dplab_exp launch --debug
```
The command updates the results to `exp.db.json`.
One can potentially view the results via
```sh
python3 scripts/view_exp_db.py
```
نیازمندی
| مقدار | نام |
|---|---|
| - | psutil |
| - | numpy |
| - | tinydb |
| - | tqdm |
| - | scipy |
| - | jpype1 |
| ==0.5.2 | diffprivlib |
| ==1.1.1 | python-dp |
| ==0.6.1 | opendp |
نحوه نصب
نصب پکیج whl dplab-0.0.7:
pip install dplab-0.0.7.whl
نصب پکیج tar.gz dplab-0.0.7:
pip install dplab-0.0.7.tar.gz