The document outlines the agenda and discussions from the RootConf data privacy conference held from April 23-29, 2021, focusing on synthetic data generation techniques. It highlights the importance of generating synthetic data to protect customer privacy while allowing for testing of machine learning features, including methods for capturing variations and correlations among data fields. Additionally, it presents the Synthetic Data Vault (SDV) as a tool for generating and managing synthetic data, addressing its capabilities and limitations.

1. Data Privacy Conference, Rootconf, 23-29 April 2021
Synthetic data
generation
Sandeep Joshi
[ needl.ai ]
https://www.linkedin.com/in/sanjoshi/

2. Data Privacy Conference, Rootconf, 23-29 April 2021
Agenda
1. Introduction to the problem
2. Capturing variation within a column
3. Capturing dependence between columns
4. Masking fields
5. Summary
2

3. Data Privacy Conference, Rootconf, 23-29 April 2021
Background on needl.ai
3

4. Data Privacy Conference, Rootconf, 23-29 April 2021
Privacy requirement
Engineers should not access customer’s data
But how do we test new features, especially ML-related ?
4
Aim : Generate Synthetic data from Production data

5. Data Privacy Conference, Rootconf, 23-29 April 2021
Real data --> Synthetic Data
5
Name Age Gender Respons
e
Tsunami
Singh
34 F Yes
Pappu
Pager
23 M Maybe
Khokha
Singh
53 F
Vasooli
Bhai
21 M
Jagat
Sahni
66 F No
Name Age Gender Respons
e
John
Smith
45 M Yes
Jack
Ryan
45 M
Jill
Reacher
34 F Maybe
Myles
Togo
23 F
Bill
Melater
18 M No

6. Data Privacy Conference, Rootconf, 23-29 April 2021
Capture variation within a column
6
Name Age Gender Response
34
23
53
21
66

7. Data Privacy Conference, Rootconf, 23-29 April 2021
Capture correlation between columns
7
Name Age Gender Response
34 F
23 M
53 F
21 M
66 F
Age and Gender may be correlated

8. Data Privacy Conference, Rootconf, 23-29 April 2021
Mask actual names
8
Name Age Gender Response
John May
April Smith
August Ryan
June Jackson
Money Spinner

9. Data Privacy Conference, Rootconf, 23-29 April 2021
SDV : Synthetic data vault
Package from MIT Data-to-AI group (https://sdv.dev/)
9

10. Data Privacy Conference, Rootconf, 23-29 April 2021
Single column
variation 10

11. Data Privacy Conference, Rootconf, 23-29 April 2021
Capture variation within column
11
Name Age Gender Response
Tsunami Singh 34 F Yes
Pappu Pager 23 M Maybe
Khokha Singh 53 F
Vasooli Bhai 21 M
Jagat Sahni 66 F No

12. Data Privacy Conference, Rootconf, 23-29 April 2021
Statistics : density estimation problem
Age
34
23
53
21
Which one ??
12
Parametric and non-parametrics methods have been invented...

13. Data Privacy Conference, Rootconf, 23-29 April 2021
Density estimation
Using scipy
13
from scipy.stats import gaussian_kde, beta
data = [5, 12, 22, 400, 800, ... ]
beta.fit(data, floc=loc, fscale=scale)
gaussian_kde(data, bw_method=’silverman’)
Demo coming up...

14. Data Privacy Conference, Rootconf, 23-29 April 2021
Dependence
between fields 14

15. Data Privacy Conference, Rootconf, 23-29 April 2021
Capture correlation between columns
15
Name Age Gender Response
Tsunami Singh 34 F Yes
Pappu Pager 23 M Maybe
Khokha Singh 53 F
Vasooli Bhai 21 M
Jagat Sahni 66 F No
Find how Age and Gender are correlated

16. Data Privacy Conference, Rootconf, 23-29 April 2021
Correlation
Both fields can change in the same/opposite direction or be unrelated
16
https://brianwhitworth.com/research-correlations/

17. Data Privacy Conference, Rootconf, 23-29 April 2021
Finding correlation with Pandas
columns_dict = {‘height’: [160, 156, 175, 180, 165, 143],
'age': [64, 55, 46, 23, 22, 19]}
df = pd.DataFrame.from_dict(columns_dict)
print(df.corr().values)
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Use Pandas df.corr()

18. Data Privacy Conference, Rootconf, 23-29 April 2021
Correlation : graphical view
18
correlation age height
age 1 0.7
height 0.7 1

19. Data Privacy Conference, Rootconf, 23-29 April 2021
How to generate correlated data
Use Copulas (Gaussian Copula, CopulaGAN, etc)
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https://en.wikipedia.org/wiki/Copula_(probability_theory)

20. Data Privacy Conference, Rootconf, 23-29 April 2021
Gaussian Copulas
Capture dependence between columns (Age vs Gender)
20
Name Age Gender Response
Transform individual columns (e.g. Age) while retaining their dependency

21. Data Privacy Conference, Rootconf, 23-29 April 2021
Demo
21
https://github.com/sanjosh/machine_learning/blob/main/copulas/synthetic%20data%20generation.ipynb
1. Capture variation within a column
2. Capture dependence between columns
3. Generate data for all columns

22. Data Privacy Conference, Rootconf, 23-29 April 2021
Masking fields 22

23. Data Privacy Conference, Rootconf, 23-29 April 2021
Masking emails
23
SDV uses Faker library https://faker.readthedocs.io/

24. Data Privacy Conference, Rootconf, 23-29 April 2021
Masking names
24
SDV uses Faker library https://faker.readthedocs.io/

25. Data Privacy Conference, Rootconf, 23-29 April 2021
SDV (synthetic
data vault) 25

26. Data Privacy Conference, Rootconf, 23-29 April 2021
SDV model for a single table
26

27. Data Privacy Conference, Rootconf, 23-29 April 2021
SDV : custom constraints
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https://github.com/sdv-dev/SDV/blob/master/tutorials/single_table_data/05_Handling_Constraints.ipynb

28. Data Privacy Conference, Rootconf, 23-29 April 2021
SDV : other features
1. Transformers for fields (convert categorical data to numbers)
2. Capture relations between tables
3. Time series
4. Metrics to compare synthetic with actual data
28
https://sdv.dev/SDV/index.html

29. Data Privacy Conference, Rootconf, 23-29 April 2021
Conclusion
SDV is a versatile tool
Good
1. Modular : can use parts of the framework (different git repos)
2. Usable with less data, unlike “deep learning”-based solutions (SDV does support GANs)
3. Its explainable (can debug or modify the output)
Issues
1. Difficult to add a custom transformer (no code samples)
2. Does not solve synthetic text generation problem (NLG)
3. Does not solve synthetic graph generation
29
Questions ?