SmartNoise SDK：用于表格数据差分隐私的工具

SmartNoise SDK 包含两个软件包：

smartnoise-sql：运行差分隐私SQL查询
smartnoise-synth：生成差分隐私合成数据

要开始使用，请参阅下面的示例。点击每个项目以查看更详细的示例。

SQL

安装

pip install smartnoise-sql

查询

import snsql
from snsql import Privacy
import pandas as pd

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

data = pd.read_csv(csv_path)
privacy = Privacy(epsilon=1.0, delta=0.01)
reader = snsql.from_connection(data, privacy=privacy, metadata=meta_path)

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

print(result)

PUMS.csv 和 PUMS.yaml 可以在 datasets 文件夹中找到。

查看 SQL 项目

合成器

安装

pip install smartnoise-synth

MWEM

import pandas as pd
import numpy as np

pums = pd.read_csv(pums_csv_path, index_col=None) # 在 datasets/ 中
pums = pums.drop(['income'], axis=1)
nf = pums.to_numpy().astype(int)

synth = snsynth.MWEMSynthesizer(epsilon=1.0, split_factor=nf.shape[1]) 
synth.fit(nf)

sample = synth.sample(10) # 获取10行合成数据
print(sample)

PATE-CTGAN

import pandas as pd
import numpy as np
from snsynth.pytorch.nn import PATECTGAN
from snsynth.pytorch import PytorchDPSynthesizer

pums = pd.read_csv(pums_csv_path, index_col=None) # 在 datasets/ 中
pums = pums.drop(['income'], axis=1)

synth = PytorchDPSynthesizer(1.0, PATECTGAN(regularization='dragan'), None)
synth.fit(pums, categorical_columns=pums.columns.values.tolist())

sample = synth.sample(10) # 合成10行数据
print(sample)