smartnoise-sql 0.2.1

Differentially Private SQL Queries

SmartNoise SQL

Differentially private SQL queries. Tested with:

• PostgreSQL
• SQL Server
• Spark
• Pandas (SQLite)
• PrestoDB

SmartNoise is intended for scenarios where the analyst is trusted by the data owner. SmartNoise uses the OpenDP library of differential privacy algorithms.

Installation

pip install smartnoise-sql

Querying a Pandas DataFrame

Use the from_df method to create a private reader that can issue queries against a pandas dataframe.

import snsql
from snsql import Privacy
import pandas as pd
privacy = Privacy(epsilon=1.0, delta=0.01)

csv_path = 'PUMS.csv'
meta_path = 'PUMS.yaml'

pums = pd.read_csv(csv_path)
reader = snsql.from_df(pums, privacy=privacy, metadata=meta_path)

result = reader.execute('SELECT sex, AVG(age) AS age FROM PUMS.PUMS GROUP BY sex')

Querying a SQL Database

Use from_connection to wrap an existing database connection.

import snsql
from snsql import Privacy
import psycopg2

privacy = Privacy(epsilon=1.0, delta=0.01)
meta_path = 'PUMS.yaml'

pumsdb = psycopg2.connect(user='postgres', host='localhost', database='PUMS')
reader = snsql.from_connection(pumsdb, privacy=privacy, metadata=meta_path)

result = reader.execute('SELECT sex, AVG(age) AS age FROM PUMS.PUMS GROUP BY sex')

Communication

• You are encouraged to join us on GitHub Discussions
• Please use GitHub Issues for bug reports and feature requests.
• For other requests, including security issues, please contact us at [e-mail].

Releases and Contributing

Please let us know if you encounter a bug by creating an issue.

We appreciate all contributions. Please review the contributors guide. We welcome pull requests with bug-fixes without prior discussion.

If you plan to contribute new features, utility functions or extensions to this system, please first open an issue and discuss the feature with us.