README.md

DP-SDV

Performance:

name	PRIV_METRIC_NumericalLR	PRIV_METRIC_NumericalMLP	PRIV_METRIC_NumericalSVR
FAST_ML-DP	0.083478628	0.1780978	0.071120968
FAST_ML	0.087402661	0.176534839	0.074326679
Gaussian Copula-DP	0.093651126	0.177785047	0.189530896
Gaussian Copula	0.066141002	0.176947755	0.073740679
CT-GAN-DP	0.166319716	0.178170664	0.189561336
CT-GAN	0.072312111	0.173496572	0.078755983
Copula-GAN-DP	0.162198436	0.179451562	0.18955882
Copula-GAN	0.084989631	0.177600009	0.07810617
TVAE-DP	0.073633603	0.176959062	0.071933513
TVAE	0.053901697	0.175550734	0.075317994

Install

Using pip:

pip install DPSDV

Quickstart

In this short tutorial we will guide you through a series of steps that will help you getting started using SDV.

1. Model the dataset using SDV

To model a multi table, relational dataset, we follow two steps. In the first step, we will load the data and configures the meta data. In the second step, we will use the sdv API to fit and save a hierarchical model. We will cover these two steps in this section using an example dataset.

Step 1: Load example data

SDV comes with a toy dataset to play with, which can be loaded using the sdv.load_demo function:

from DPSDV import load_demo

metadata, tables = load_demo(metadata=True)

This will return two objects:

1. A Metadata object with all the information that SDV needs to know about the dataset.

For more details about how to build the Metadata for your own dataset, please refer to the Working with Metadata tutorial.

2. A dictionary containing three pandas.DataFrames with the tables described in the metadata object.

The returned objects contain the following information:

{
    'users':
            user_id country gender  age
          0        0     USA      M   34
          1        1      UK      F   23
          2        2      ES   None   44
          3        3      UK      M   22
          4        4     USA      F   54
          5        5      DE      M   57
          6        6      BG      F   45
          7        7      ES   None   41
          8        8      FR      F   23
          9        9      UK   None   30,
  'sessions':
          session_id  user_id  device       os
          0           0        0  mobile  android
          1           1        1  tablet      ios
          2           2        1  tablet  android
          3           3        2  mobile  android
          4           4        4  mobile      ios
          5           5        5  mobile  android
          6           6        6  mobile      ios
          7           7        6  tablet      ios
          8           8        6  mobile      ios
          9           9        8  tablet      ios,
  'transactions':
          transaction_id  session_id           timestamp  amount  approved
          0               0           0 2019-01-01 12:34:32   100.0      True
          1               1           0 2019-01-01 12:42:21    55.3      True
          2               2           1 2019-01-07 17:23:11    79.5      True
          3               3           3 2019-01-10 11:08:57   112.1     False
          4               4           5 2019-01-10 21:54:08   110.0     False
          5               5           5 2019-01-11 11:21:20    76.3      True
          6               6           7 2019-01-22 14:44:10    89.5      True
          7               7           8 2019-01-23 10:14:09   132.1     False
          8               8           9 2019-01-27 16:09:17    68.0      True
          9               9           9 2019-01-29 12:10:48    99.9      True
}

Step 2: Fit a model using the SDV API.

First, we build a hierarchical statistical model of the data using SDV. For this we will create an instance of the sdv.SDV class and use its fit method.

During this process, SDV will traverse across all the tables in your dataset following the primary key-foreign key relationships and learn the probability distributions of the values in the columns.

from DPSDV.relational import HMA1

model = HMA1(metadata)
model.fit(tables)

OR

from DPSDV.relational import HMA1

model = HMA1(metadata)
model.fit(tables, eps=1e2)

to add differential privacy epsilon through argument eps=1e2

Once the modeling has finished, you can save your fitted model instance for later usage using the save method of your instance.

model.save('sdv.pkl')

The generated pkl file will not include any of the original data in it, so it can be safely sent to where the synthetic data will be generated without any privacy concerns.

2. Sample data from the fitted model

In order to sample data from the fitted model, we will first need to load it from its pkl file. Note that you can skip this step if you are running all the steps sequentially within the same python session.

model = HMA1.load('sdv.pkl')

After loading the instance, we can sample synthetic data by calling its sample method.

samples = model.sample()

The output will be a dictionary with the same structure as the original tables dict, but filled with synthetic data instead of the real one.

Implementations

1. Tabular Preset

So, adding noise based on the Wishart Mechanism for Differentially Private Principal Components Analysis paper's algorithm 1. Lap(0, 2d/ne), in this d is the number of columns in the covariance matrix taken from model.get_parameters(). Now, taking sensitivity=1. We modify the covariance matrix.

2. GaussianCopula Model

So, adding noise based on the Wishart Mechanism for Differentially Private Principal Components Analysis paper's algorithm 1. Lap(0, 2d/ne), in this d is the number of columns in the covariance matrix taken from model.get_parameters(). Now, taking sensitivity=1. We modify the covariance matrix.

3. CTGAN Model

Added DP-SGD

4. CopulaGAN Model

Added DP-SGD

5. TVAE Model

Added DP-SGD

6. MWEM Model

Added DP privacy