PipelineDP is a framework for applying differentially private aggregations to large datasets using batch processing systems such as Apache Spark, Apache Beam, and more.
To make differential privacy accessible to non-experts, PipelineDP:
- Provides a convenient API familiar to Spark or Beam developers.
- Encapsulates the complexities of differential privacy, such as:
- protecting outliers and rare categories,
- generating safe noise,
- privacy budget accounting.
- Supports many standard computations, such as count, sum, and average.
Additional information can be found at pipelinedp.io.
Note that this project is still experimental and is subject to change. At the moment we don't recommend its usage in production systems as it's not thoroughly tested yet. You can learn more in the Roadmap section.
The project is a collaboration between OpenMined and Google in an effort to bring Differential Privacy to production.
Here are some examples of how to use PipelineDP:
Please check out the codelab for a more detailed demonstration of the API functionality and usage.
Code sample showing private processing on Spark:
# Define the privacy budget available for our computation.
budget_accountant = pipeline_dp.NaiveBudgetAccountant(total_epsilon=1,
total_delta=1e-6)
# Wrap Spark's RDD into its private version. You will use this private wrapper
# for all further processing instead of the Spark's RDD. Using the wrapper ensures
# that only private statistics can be released.
private_movie_views = \
make_private(movie_views, budget_accountant, lambda mv: mv.user_id)
# Calculate the private sum of ratings per movie
dp_result = private_movie_views.sum(
SumParams(
# The aggregation key: we're grouping data by movies
partition_extractor=lambda mv: mv.movie_id,
# The value we're aggregating: we're summing up ratings
value_extractor=lambda mv: mv.rating,
# Limits to how much one user can contribute:
# .. at most two movies rated per user
# (if there's more, randomly choose two)
max_partitions_contributed=2,
# .. at most one ratings for each movie
max_contributions_per_partition=1,
# .. with minimal rating of "1"
# (automatically clip the lesser values to "1")
min_value=1,
# .. and maximum rating of "5"
# (automatically clip the greater values to "5")
max_value=5)
)
budget_accountant.compute_budgets()
# Save the results
dp_result.saveAsTextFile(FLAGS.output_file)PipelineDP without any frameworks:
pip install pipeline-dp
If you like to run PipelineDP on Apache Spark:
pip install pipeline-dp pyspark
on Apache Beam:
pip install pipeline-dp apache-beam.
Supported Python version >= 3.7.
Note for Apple Silicon users: PipelineDP pip package is currently available only for x86 architecture. The reason is that PyDP does not have pip pacakge. It might be possible to compile it from sources for Apple Silicon.
To install the requirements for local development, run make dev.
Please run make precommit to auto-format, lint check, and run tests.
Individual targets are format, lint, test, clean, dev.
We use the Google Python Style Guide.
When developing it is convenient to run an end-to-end example. To do this:
-
Download Netflix prize dataset from https://www.kaggle.com/netflix-inc/netflix-prize-data and unpack it.
-
The dataset itself is pretty big, to speed-up the run it's better to use a sample of it. Here's how to take a subset of the data in bash:
head -10000 combined_data_1.txt > data.txt -
Run
python run_all_frameworks.py --input_file=<path to data.txt from 2> --output_file=<...>
If you have questions about the PipelineDP, join OpenMined's Slack and check the #differential-privacy channel.