02_khaled_el_emam_en.pptx

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This document discusses the use of synthetic data as an alternative to sharing real data. It describes how synthetic data is generated using a data simulator to replicate real data distributions while protecting privacy. The key benefits of synthetic data are that it allows data sharing without privacy risks, and various methods like partial synthesis and full synthesis can be used to control the privacy-utility tradeoff. Proper techniques and controls need to be applied to generate synthetic data that sufficiently preserves the utility of the original data.

Engineering

Data Synthesis:
A Tool for Responsible
Data Sharing
Khaled El Emam
16th June 2021

(c) Copyright 2019-2021 Replica Analytics Ltd.
Agenda
2
General description of what
synthetic data is and general use
cases
1
Introduction to Synthesis
2 An examination of privacy risks and
the utility of synthetic data
Privacy and Utility
3 A brief look at methods for the
generation of synthetic data
Methods

(c) Copyright 2019-2021 Replica Analytics Ltd.
Synthetic Data Uses
• Data Sharing and Data Access
• AI and data science projects
• Software testing
• Proof of concept and technology evaluations
• Open data/open science
• Hackathons and data competitions/challenges
• Data Amplification and Data Augmentation
• Amplifying small datasets
• Correct bias
3

(c) Copyright 2019-2021 Replica Analytics Ltd.
The Synthesis Process
4

(c) Copyright 2019-2021 Replica Analytics Ltd.
Data Simulator
5
Allows generation of synthetic data without direct
access to real data

(c) Copyright 2019-2021 Replica Analytics Ltd.
Simulator Exchange
6
Data Consumers

(c) Copyright 2019-2021 Replica Analytics Ltd.
Two Synthesis Strategies
7
Partial
Synthesis
Synthesize
quasi-identifiers
Synthesis
Full Synthesis
Synthesize all
variables
Synthesis

(c) Copyright 2019-2021 Replica Analytics Ltd.
Identifiability Spectrum
8

(c) Copyright 2019-2021 Replica Analytics Ltd.
Privacy Risks
9
Dataset Fully Synthetic Data Original Data
Washington Hospital
Data
0.0197 0.098
Canadian COVID Data 0.0086 0.034
A commonly used risk threshold = 0.09

(c) Copyright 2019-2021 Replica Analytics Ltd.
Privacy-Utility Tradeoff
10

(c) Copyright 2019-2021 Replica Analytics Ltd.
Distribution Comparisons
11

(c) Copyright 2019-2021 Replica Analytics Ltd.
Mortality Over Time
12

(c) Copyright 2019-2021 Replica Analytics Ltd.
Mortality By Age
13

(c) Copyright 2019-2021 Replica Analytics Ltd.
Utility Framework
• An important concern of data
users is the data utility
• Utility has multiple dimensions to it
• Synthetic data may be optimized
on multiple utility dimensions
simultaneously to meet the needs
of multiple users, or on single
dimensions to address the needs
of limited users
14

(c) Copyright 2019-2021 Replica Analytics Ltd.
Risk-based Approach
15

(c) Copyright 2019-2021 Replica Analytics Ltd.
Risk-based Approach
16
• Generalization
• Suppression
• Addition of noise
• Microaggregation

(c) Copyright 2019-2021 Replica Analytics Ltd.
Risk-based Approach
17
• Security controls
• Privacy controls
• Contractual controls

(c) Copyright 2019-2021 Replica Analytics Ltd.

(c) Copyright 2019-2021 Replica Analytics Ltd.
The Erosion of Trust
21

(c) Copyright 2019-2021 Replica Analytics Ltd.
Skill Set
• The skills needed to create
de-personalized datasets
are very specialized, take
time to develop, and
generally difficult to find
cost-effectively
• This limits the ability to scale
• Synthesis requires minimal
skills in practice – it is a
computationally intensive
process
22

(c) Copyright 2019-2021 Replica Analytics Ltd.
Regulatory Questions
• Is synthetic data considered non-identifiable
information ?
• Does the act of converting identifiable information into
non-identifiable synthetic information require
additional consent or authorization ?
• Can a data custodian outsource the creation of
synthetic data ?
• Can synthetic data be used for any purpose ?
23

(c) Copyright 2019-2021 Replica Analytics Ltd.
Sequential Synthesis
24

(c) Copyright 2019-2021 Replica Analytics Ltd.
Variational Auto Encoder (VAE)
25

(c) Copyright 2019-2021 Replica Analytics Ltd.
Generative Adversarial Network (GAN)
26

TOOLBOX OF TECHNIQUES
cc: tunnelarmr - https://www.flickr.com/photos/27311060@N00

QUESTIONS
cc: an untrained eye - https://www.flickr.com/photos/26312642@N00

(c) Copyright 2019-2021 Replica Analytics Ltd.
References
• Z. Azizi, C. Zheng, L. Mosquera, L. Pilote, K. El Emam: “Replicating Secondary
Studies Using Synthetic Clinical Trial Data”, BMJ Open, 11:e043497, 2021.
• K. El Emam, L. Mosquera, E. Jonker, H. Sood: “Evaluating the Utility of Synthetic
COVID-19 Case Data”, JAMIA Open, 14(1):ooab012, January 2021.
• K. El Emam, L. Mosquera, and C. Zheng, “Optimizing the Synthesis of Clinical Trial
Data Using Sequential Trees,” JAMIA, 28(1): 3-13, 2021.
• K. El Emam, L. Mosquera, and J. Bass, “Evaluating Identity Disclosure Risk in Fully
Synthetic Health Data: Model Development and Validation,” JMIR, vol. 22, no. 11, Nov.
2020. [Online]. Available: https://www.jmir.org/2020/11/e23139.
• K. El Emam, L. Mosquera, and R. Hoptroff, Practical Synthetic Data Generation:
Balancing Privacy and the Broad Availability of Data. O’Reilly, 2020.
• K. El Emam, “Seven Ways to Evaluate the Utility of Synthetic Data,” IEEE Security and
Privacy, July/August, 2020.
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02_khaled_el_emam_en.pptx

1. Data Synthesis: A Tool for Responsible Data Sharing Khaled El Emam 16th June 2021

2. (c) Copyright 2019-2021 Replica Analytics Ltd. Agenda 2 General description of what synthetic data is and general use cases 1 Introduction to Synthesis 2 An examination of privacy risks and the utility of synthetic data Privacy and Utility 3 A brief look at methods for the generation of synthetic data Methods

3. (c) Copyright 2019-2021 Replica Analytics Ltd. Synthetic Data Uses • Data Sharing and Data Access • AI and data science projects • Software testing • Proof of concept and technology evaluations • Open data/open science • Hackathons and data competitions/challenges • Data Amplification and Data Augmentation • Amplifying small datasets • Correct bias 3

9. (c) Copyright 2019-2021 Replica Analytics Ltd. Privacy Risks 9 Dataset Fully Synthetic Data Original Data Washington Hospital Data 0.0197 0.098 Canadian COVID Data 0.0086 0.034 A commonly used risk threshold = 0.09

14. (c) Copyright 2019-2021 Replica Analytics Ltd. Utility Framework • An important concern of data users is the data utility • Utility has multiple dimensions to it • Synthetic data may be optimized on multiple utility dimensions simultaneously to meet the needs of multiple users, or on single dimensions to address the needs of limited users 14

22. (c) Copyright 2019-2021 Replica Analytics Ltd. Skill Set • The skills needed to create de-personalized datasets are very specialized, take time to develop, and generally difficult to find cost-effectively • This limits the ability to scale • Synthesis requires minimal skills in practice – it is a computationally intensive process 22

23. (c) Copyright 2019-2021 Replica Analytics Ltd. Regulatory Questions • Is synthetic data considered non-identifiable information ? • Does the act of converting identifiable information into non-identifiable synthetic information require additional consent or authorization ? • Can a data custodian outsource the creation of synthetic data ? • Can synthetic data be used for any purpose ? 23

27. TOOLBOX OF TECHNIQUES cc: tunnelarmr - https://www.flickr.com/photos/27311060@N00

28. QUESTIONS cc: an untrained eye - https://www.flickr.com/photos/26312642@N00

29. (c) Copyright 2019-2021 Replica Analytics Ltd. References • Z. Azizi, C. Zheng, L. Mosquera, L. Pilote, K. El Emam: “Replicating Secondary Studies Using Synthetic Clinical Trial Data”, BMJ Open, 11:e043497, 2021. • K. El Emam, L. Mosquera, E. Jonker, H. Sood: “Evaluating the Utility of Synthetic COVID-19 Case Data”, JAMIA Open, 14(1):ooab012, January 2021. • K. El Emam, L. Mosquera, and C. Zheng, “Optimizing the Synthesis of Clinical Trial Data Using Sequential Trees,” JAMIA, 28(1): 3-13, 2021. • K. El Emam, L. Mosquera, and J. Bass, “Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation,” JMIR, vol. 22, no. 11, Nov. 2020. [Online]. Available: https://www.jmir.org/2020/11/e23139. • K. El Emam, L. Mosquera, and R. Hoptroff, Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data. O’Reilly, 2020. • K. El Emam, “Seven Ways to Evaluate the Utility of Synthetic Data,” IEEE Security and Privacy, July/August, 2020. 29

Editor's Notes

Getting started with the generation of synthetic data: There are many ways to generate synthetic data, but high-level synthetic data is generated by fitting a model to real data then sampling from the model to generate synthetic records. So for example, these faces are actually synthetic images that have been generated. For synthetic data the goal is to generate new records that look realistic, but in the context of datasets that include personal information we are also interested in preserving the privacy of individuals in the real data. So synthetic data generating needs to balance preserving privacy with generating high utility data that looks realistic. This is especially true in the context of clinical trial data.
Getting started with the generation of synthetic data: There are many ways to generate synthetic data, but high-level synthetic data is generated by fitting a model to real data then sampling from the model to generate synthetic records. So for example, these faces are actually synthetic images that have been generated. For synthetic data the goal is to generate new records that look realistic, but in the context of datasets that include personal information we are also interested in preserving the privacy of individuals in the real data. So synthetic data generating needs to balance preserving privacy with generating high utility data that looks realistic. This is especially true in the context of clinical trial data.

02_khaled_el_emam_en.pptx

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Editor's Notes