1. Dr. Michael Platzer gave a guest lecture at Imperial College London on synthetic data, covering what it is, how accurate it can be, and how safe it is. 2. He discussed how to generate synthetic data that is statistically representative of real data while being truly anonymous using MOSTLY AI's techniques. 3. Dr. Platzer evaluated the accuracy of synthetic data using measures like comparing machine learning model performance on synthetic versus real data as well as comparing marginal distributions, finding synthetic data can match real data closely.

1. Dr. Michael Platzer, MOSTLY AI
Nov 2023
Guest Lecture at Imperial College London
Everything You Always Wanted
to Know About Synthetic Data
in 45 minutes 😳

2. What is it?
How accurate is it?
How safe is it?
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Synthetic Data
1.
2.
3.
Give it a try!
4.

3. 3
We’re MOSTLY AI, and we’re all about…

4. useful, but
re-identifiable
private, but
useless
Let’s try to anonymize this guy!
4

5. Let’s create some new faces!
5
random-generated
400px
300px
model-generated
400px
300px
AI-generated
400px
300px
self-generated
400px
300px

6. 6
Privacy-Sensitive Structured Data
source: https://archive.ics.uci.edu/ml/datasets/census+income, ~49k records, 421 billion combinations possible
󰢃
access denied 💣

7. 7
Let’s create some Synthetic Data!
→ https://www.mostly.ai/
Upload 󰟑
1. Synthesize 💫
2. Done ✔ in ~50secs
3.

8. 8
Representative Synthetic Data
statistically representative, highly realistic, truly anonymous synthetic data - at any volume

9. Representative Synthetic Data for Everyone
Actual Data
privacy-restricted
biased
incomplete
Synthetic Data
realistic
representative
truly anonymous
granular level
Data Consumers
people & algorithms

10. Representative Synthetic Data for Everything
for Exploration
for Analytics
for AI Training
for AI Validation

11. How good is it?
11
vs.
How close is the synthetic data to the original?
What does it mean to be close for datasets?
And, then how close should it even be?
[there is surprisingly little consensus on answering this question]
Original Data Synthetic Data

12. Automated Empirical Quality Assurance
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See also our paper on Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data and our blog post series on accuracy and privacy.

13. How accurate is it?
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1. Turing Tests - is it fake or not?
Measures realism, i.e. the rule-adherence, of synthetic samples at record-level. Can be performed by
humans as well as machines. But it doesn’t inform about the statistical representativeness.
2. Compare Utility for Machine Learning
Train on synthetic, test on a real holdout data for a specific ML task, and compare predictive accuracy
to the same model being trained on the original data. Strong test, but only checks for one specific
signal in the data.
3. Measure Deviations in Marginal Distributions
Calculate lower-level marginal empirical distributions, and systematically measure any deviations
thereof.

14. How accurate is it?
14
Univariate Distributions

15. How accurate is it?
15
Bivariate Distributions

16. How accurate is it?
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17. How accurate is it? - Train-Synthetic Test-Real
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Holdout Data
(20%)
Training Data
(80%)
Synthesize
Synthetic Data
(eg. 10x)
Train ML Model
(on synthetic)
Train ML Model
(on real)
Evaluate on Holdout
Evaluate on Holdout
Compare

18. How accurate is it?
18
→ https://mostly.ai/blog/boost-machine-learning-accuracy-with-synthetic-data/

19. Case Study: American Community Survey
19
→ Amp It Up: synthetic population data at scale
1.5 million actual records with 32 attributes ⚠

20. Case Study: American Community Survey
20
→ Amp It Up: synthetic population data at scale
1.5 million synthetic records with 32 attributes ✅

21. Case Study: American Community Survey
21
→ Amp It Up: synthetic population data at scale
1.5 million synthetic records with 32 attributes ✅
Identical
Model Performance

22. Case Study: American Community Survey
22
→ Amp It Up: synthetic population data at scale
1.5 million synthetic records with 32 attributes ✅
Identical
Driver Analysis

23. Case Study: American Community Survey
23
→ Amp It Up: synthetic population data at scale
1.5 million synthetic records with 32 attributes ✅
Identical
Driver Analysis

24. 1. Holdout-Based Comparisons
2. Shadow Model Comparisons
3. Distance-Based Comparisons
How safe is it?
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25. How safe is it? - Holdout-Based Comparisons
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https://github.com/statice/anonymeter
Example
rcontrol
= 50%
rtrain
= 55%
R = 0.10

26. How safe is it? - Shadow Models
26
ST
synthesize
ST’
T X
X
T’
X
X
X
X
?
?
Δ difference?
synthesize
infer
infer
exclude
subject
-
We shall not be able to
infer anything more about
an individual, given that
that person was included
in the database used for
synthetization.

27. Training on Actual Data T → Information Leak
Training on Synthetic Data S → No Information Leak (plus on-par performance)
https://mostly.ai/blog/truly-anonymous-synthetic-data-legal-definitions-part-ii
How safe is it? - Shadow Models for Attribute Inference
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Accuracy scores for 50 randomly chosen subjects, that were part of training
Target T
Synthetic ST
Accuracy scores for 50 randomly chosen subjects, that were NOT part of training
T’arget
Target T’
Synthetic ST’
T vs T’ → 44% > 39%
S vs S’→ 38% ~ 39%

28. How safe is it? - Shadow Models for Membership Inference
28
MOSTLY AI
Successful attack ??

29. How safe is it? - Distance-Based Comparisons
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Idea: Synthetic subjects shall not be “too close” to actual subjects, i.e. shall be sufficiently distinct.
Distances between actual subjects serve as reference.
Actual
Subject
Actual
Nearest Neighbor
Synthetic
Nearest Neighbor
DCRS
DCR
T
Distance-to-Closest-Records

30. How safe is it?
30
1. Holdout-Based Comparisons → requires holdout
2. Shadow Model Comparisons → heavy compute
3. Distance-Based Comparisons → easy compute & interpret

31. Learn More
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→ https://mostly.ai/docs/tutorials
● Synthetic Behavioral Data
● Synthetic Geo Data
● Synthetic Text Data
● Fair Synthetic Data
● Synthetic Data Benchmarks
● JRC Report on Synthetic Data
● AI-based Re-Identification Attacks
● Privacy Assessment of Synthetic Data
● and so much more…

32. Give it a try!
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#GoSynthetic
→ https://synthetic.mostly.ai/

33. 33
Important Note 1
The baseline for Attribute Disclosure is any inference
based on a world that doesn’t know about an individual.
Naturally, the privacy of a person, that doesn’t exist, can
not be already violated. Yet, some SD critics mix up
attribute inference with privacy.

34. Important Note 2
Meta-Data (=value ranges) need to be protected to be
safe against trivial membership inference attacks. Most of
the open-source as well as custom-coded solutions don’t
protect against that. Then the existence of a “US President”
in a synthesized cancer dataset would already leak privacy.
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35. Important Note 3
Privacy needs to be protected at subject level, and not at
event level. No privacy check will ever be able to know by
itself whether that was the case or not. Thus, context
information on the data itself is always required to assess
privacy.
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