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Presentations, Articles, Reports & Media
This page is regularly updated with new videos, presentations, documents, articles, white papers and other information from Replica Analytics. Check back often to get the latest resource downloads.
Article
Synthetic Data is Key to Privacy by Design Practices in New Canadian Smart City Partnership
Article
Real-World Evidence Firm Aetion Grabs Privacy-Protected Synthetic Data Provider Replica Analytics
Evaluating Privacy Risks in Synthetic Data Using Membership Disclosure
- December 1, 2022
Lucy Mosquera & Xi Fang
Synthetic data generation can generate datasets with low privacy risk. Quantifying privacy risks in synthetic data is an essential step in building trust in this innovative technology. This webinar offers an introduction to the topic and new findings with practical guidance for membership disclosure risk assessments in synthetic data.
On the Validity of Statistical Analyses with Privacy-Preserving Synthetic Data
- September 29, 2022
Khaled El Emam, SVP & GM, Replica Analytics; Lucy Mosquera, Director Data Science, Replica Analytics
In this ISPOR webinar we gave an overview of synthetic data and its privacy preserving properties, advantages over traditional de-identification, and then reviewed results from a simulation of the validity of inference on synthetic oncology datasets.
There is no charge to view but registering on the site is required.
Synthetic Data: The Future of Data Sharing?
- September 13, 2022
Khaled El Emam, SVP & GM, Replica Analytics; Elizabeth Denham CBE, Baker McKenzie LLP.
Hosted by Baker McKenzie LLP, this webinar covered how synthetic data are generated, different use cases & opportunities, assessing privacy, utility & risks, and regulatory issues.
Exploring the concept of Identifiability in Canada’s proposed Consumer Privacy Protection Act (Bill C-27)
- July 21, 2022
Khaled El Emam, SVP & GM, Replica Analytics; Adam Kardash, Partner, Osler/AccessPrivacy
Co-hosted by OslerLLP/ AccessPrivacy, this webinar focused on the concepts relating to identifiability as well as the definitions for de-identification and anonymization proposed in Bill C-27, and the implications for generating, using, and disclosing non-identifiable data.
Assessing Re-identification Risk Using Synthetic Data
- June 29, 2022
Lucy Mosquera, Director of Data Science, Replica Analytics
In this webinar, Replica’s Director of Data Science, Lucy Mosquera, presents a novel approach to employ synthetic data for a more accurate assessment of re-identification risks in datasets, to better manage privacy risks and enable greater data sharing in healthcare and other sectors.
Synthetic Data as a Privacy Enhancing Technology
- May 25, 2022
Lucy Mosquera, Director of Data Science, Replica Analytics
This was a presentation delivered at the State of Play of De-identification Techniques Masterclass, organized by the Future of Privacy Forum, at the 15th International Computers, Privacy & Data Protection Conference in Brussels. All Masterclass presentations here.
Generating Synthetic Longitudinal Data
- May 18, 2022
Lucy Mosquera, Director of Data Science, Replica Analytics
This webinar hosted by C-Path provides an introduction to synthetic data generation – what it means, how it works and what technologies are used – as well as an overview of use cases where synthetic data can provide value in the context of RWD and clinical trial data.
Utility Assessments in Synthetic Data
- April 27, 2022
Lucy Mosquera & Xi Fang
This webinar explores ways to assess utility in synthetic data and shares new research. We identify which utility assessments are most predictive of analytic value and show which can best rank synthesis methods for performance on a realistic biostatistics analysis. The study was funded by multiple Canadian government research funding agencies and the Bill and Melinda Gates Foundation.
Managing and Regulating Privacy Risks in Synthetic Data
- March 30, 2022
Khaled El Emam, SVP & GM, Replica Analytics; Lucy Mosquera, Director Data Science, Replica Analytics
In this webinar, Khaled El Emam and Lucy Mosquera highlight the main findings of a year-long study done with the CHEO Research Institute and the University of Ottawa. Its focus is the privacy assurance use case for synthetic data and the underlying conclusions come from a technical analysis of the different types of privacy risks, a legal analysis of these risks, as well as perspectives shared by regulators.
Synthetic Data Generation 101
- February 9, 2022
Khaled El Emam, SVP & GM, Replica Analytics
In this webinar, Dr. Khaled El Emam provides a general introduction to synthetic data generation (SDG), explaining what it means, how it works and what technologies are used, as well as an overview of the use cases where synthetic data can provide value in the context of real-world data and clinical trial data.
Generating Synthetic Longitudinal Data
- December 1, 2021
Khaled El Emam, SVP & GM, Replica Analytics; Lucy Mosquera, Director Data Science, Replica Analytics
Longitudinal data differ from other types of data to which synthesis has been applied, such as tabular data, necessitating a novel set of approaches and technologies which we illustrate in this webinar.
Synthetic Data Generation for Rare Disease Research
- October 27, 2021
Khaled El Emam & Jason Colquitt
This webinar, presented by Dr. Khaled El Emam and Jason Colquitt, discusses the small data challenges of rare disease research and how synthetic data generation can help address these challenges.
Measuring Re-identification Risk for Synthetic and Anonymized Data
- September 22, 2021
Khaled El Emam, SVP & GM, Replica Analytics
Khaled El Emam discusses accurate methods for measuring re-identification risk. This new class of risk estimators helps avoid issues experienced with many of the current risk measurement methods.
Synthetic Data
- September 13, 2021
Dean Eurich, Program Lead, Clinical Epidemiology, and Professor, School of Public Health
A presentation by Dean Eurich, Professor, School of Public Health, University of Alberta. Presented at the 2021 PHUSE/FDA Computational Science Symposium (CSS), Professor Eurich discusses how synthetic data can be used to accelerate access to data for health research.
Practical Mechanisms for Generating Anonymous Data
- September 2, 2021
Khaled El Emam, SVP & GM, Replica Analytics
This BIH Digital Medicine online lecture is an overview of risk-based methods for creating non-identifiable data, highlights some limitations, discusses SDG methods and their applications in practice.
Ten Recommendations For Regulating De-identification
- September 1, 2021
Khaled El Emam, SVP & GM, Replica Analytics
Based on his almost two decades of experience, Khaled El Emam presents ten key recommendations on how to define the criteria for when information becomes non-identifiable, and how to regulate the resulting data.
Implementing the FAIR Data Sharing Principles
- June 17, 2021
Khaled El Emam, SVP & GM, Replica Analytics; David Sibbald, CEO and Co-Founder, Aridhia; Rodrigo Barnes, CTO, Aridhia
Presentation of Replica Analytics’ joint offering with Aridhia: An integrated and managed FAIR data sharing platform.
Data Synthesis: A Tool for Responsible Data Sharing
- June 16, 2021
Khaled El Emam, SVP & GM, Replica Analytics
This presentation by Khaled El Emam provides a general overview of synthetic data generation and its applications.
Experiences Implementing Data Synthesis in a Global Life Sciences Company
- June 16, 2021
Stephen Bamford, Head of Clinical Data Standards & Transparency; IDAR, Global Development, Janssen
A webinar from Stephen Bamford at Janssen describing their experiences with the implementation of synthetic data generation and where it fits in among other PETs.
What We Learned About Data Synthesis in 2020
- May 20, 2020
Khaled El Emam, SVP & GM, Replica Analytics
In this webinar, Khaled El Emam provided his perspective on the changes in 2020 in data synthesis technology, the market, the adoption, the practical application, the governance, and the potential of this technology.
Empirical Assessment of Privacy Risks in Data
- March 25, 2020
Khaled El Emam, SVP & GM, Replica Analytics; Janice Branson, Global Head of Advanced Methodology & Data Science, Novartis; Nathan Good, Principal, Good Research
Presentation on our experiences with trying to identify individuals in datasets that have been de-personalized, especially health data. This is an overview of Motivated Intruder Attacks.
Ten Things I Have Learned About Health Data Monetization
- February 12, 2020
Khaled El Emam, SVP & GM, Replica Analytics
Sharing some key experiences working with organizations to monetize their data for commercial, academic, and public interest purposes. A practical journey with many lessons learned.
Ten Things I Have Learned About De-identification
- January 15, 2020
Khaled El Emam, SVP & GM, Replica Analytics
An overview of lessons learned and key observations from more than a decade of developing de-identification and other privacy enhancing technologies and applying them in practice globally.
Synthetic Clinical Trial Data: Use Cases, Methods, and Experiences
- December 9, 2020
Stephen Bamford, Head of Clinical Data Standards & Transparency; IDAR, Global Development, Janssen
Sharing experiences with the synthesis of clinical trial data and how that data has been used by a pharmaceutical company.
Optimal Synthesis of Clinical Trial Data
- November 4, 2020
Khaled El Emam, SVP & GM, Replica Analytics
Presentation describing a method for the synthesis of complex clinical trial data and explaining how to tune its hyperparameters, with results presented on multiple oncology clinical trial datasets.
An Introduction to Synthetic Clinical Trial Data
- October 4, 2019
Richard Hoptroff, Founder and CTO, Hoptroff; Lucy Mosquera, Director Data Science, Replica Analytics; Rebecca Li, Executive Director, Vivli; Ben Szekely, Director of Product, Cambridge Semantics
An overview of lessons learned and key observations from more than a decade of developing de-identification and other privacy enhancing technologies and applying them in practice globally.
2022 Symposium: Perspectives on Regulating Privacy Enhancing Technologies
A Practical Introduction to Synthetic Data Generation
- May 25, 2022
Khaled El Emam, University of Ottawa and Replica Analytics
De-identification Standardization Efforts by CANON
- May 25, 2022
Adam Kardash, Osler/Access Privacy & CANON
Regulating
Non-identifiable Data
and Synthetic Data
- May 25, 2022
Vance Lockton, Office of the Information and Privacy Commissioner of Ontario
2021 Summit - Synthetic Data: The Future of Data Sharing
The Alberta Synthetic Data Project
- July 7, 2021
Dean Eurich, Professor, School of Public Health, University of Alberta
Lucy Mosquera, Director of Data Science, Replica Analytics
Uses of Synthetic Data by the Life Sciences Industry
- July 7, 2021
Panellists Virginie Giroux, HEOR, Merck; Stephen Bamford, Janssen; Janice Branson, Novartis; moderator Reg Joseph, Health Cities.
How Synthetic Data will Transform Health Research and Innovation
- July 7, 2021
Karen Cuenco, Integration & Quantitative Science, Senior Specialist Bill and Melinda Gates Foundation
2020 Summit - Getting Access to COVID-19 Data
Responsibly Sharing COVID-19 Data
- July 15, 2020
Khaled El Emam, University of Ottawa and Replica Analytics
The Pandemic Menace and the Future of Humanity Through the Eyes of a Realist
- July 15, 2020
Gerry Stegmaier, Reed Smith
Synthetic data as an enabler for machine learning applications in medicine
- November 2022
There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of synthetic data generation for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes.
Validating A Membership Disclosure Metric For Synthetic Health Data
- October 2022
One of the increasingly accepted methods to evaluate the privacy of synthetic data is by measuring the risk of membership disclosure. This is a measure of the F1 accuracy that an adversary would correctly ascertain that a target individual from the same population as the real data is in the dataset used to train the generative model, and is commonly estimated using a data partitioning methodology with a 0.5 partitioning parameter.
Measuring re-identification risk using a synthetic estimator to enable data sharing
- June 2022
One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population.
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study
- April 2022
JMIR Medical Informatics published this study which evaluates the ability of common utility metrics to rank synthetic data generation methods according to performance on a specific analytic workload. The workload of interest is the use of synthetic data for logistic regression prediction models, which is a very frequent workload in health research.
Can Synthetic Data Be A Proxy For Real Clinical Trial Data? A Validation Study
- April 2021
A detailed case study demonstrating the high utility of synthetic data generation for a colon cancer clinical trial dataset.
Evaluating the Utility of Synthetic COVID-19 Case Data
- March 2021
A detailed case study demonstrating the high utility and low privacy risks of synthetic data generation for the Ontario COVID-19 case dataset.
Evaluating Identity Disclosure Risk in Fully Synthetic Health Data
- November 2020
Outlines a privacy model that measures attribute disclosure conditional on identity disclosure for synthetic data, combining two types of disclosure risk within a single framework.
Optimizing the Synthesis of Clinical Trial Data Using Sequential Trees
- November 2020
This paper describes the basic method for generating synthetic data for small source datasets, such as for clinical trials.
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