Guide to the De-Identification of Personal Health Information (Hardcover)

The growing availability of health data has created unprecedented opportunities for research, public health, quality improvement, and data-driven innovation. At the same time, health information is among the most sensitive forms of personal data, and its use raises legal, ethical, and regulatory challenges.

This book helps the reader understand, assess, and mitigate privacy risks to facilitate responsible health data use and sharing. It presents a comprehensive, risk-based de-identification methodology. Rather than treating de-identification as a simple removal of identifiers, it conceptualizes it as a structured risk management process that reduces the likelihood of re-identification to a very low level within a given data-sharing context. The methodology integrates legal, technical, and organizational perspectives and aligns with international standards and regulatory guidance. It is also based on more than two decades of performing risk assessments and de-identification of health data globally.

Foundational chapters examine the life cycle of health data, data subject perspectives, and the legal frameworks governing the use and disclosure of personal health information across jurisdictions. Building on this context, the book develops a conceptual model of risk that incorporates adversaries, attack scenarios, and different forms of disclosure, including identity, attribute, and membership disclosure. It reviews transformation methods, including emerging technologies such as synthetic data, to effectively reduce data vulnerability, and algorithmic approaches that optimize the balance between privacy protection and analytical utility.

A central contribution of the book is its detailed methodology for quantitatively measuring re-identification risk. It introduces formal models for threat modeling, including adversary motives, capacity, and background knowledge, along with statistical metrics for estimating vulnerability at a dataset level. Such a quantitative model supports standardized and repeatable risk assessments with transparent decision rules and risk thresholds that guide whether and how data should be transformed before release.

Case studies, assessment instruments, and operational strategies support organizations in applying these techniques consistently and defensibly.

Designed for a broad audience, including data custodians, researchers, privacy professionals, regulators, and health data practitioners, this book provides both the conceptual foundations and practical tools needed to enable responsible secondary use of health data while preserving privacy and maintaining the value of data.

Dr. Lisa Pilgram is a Postdoctoral Fellow at the School of Epidemiology and Public Health at the University of Ottawa, Head of Operations at the Ottawa Medical AI Research Institute, and a Clinician Scientist at Charité – Universitätsmedizin Berlin. She conducts research within the Electronic Health Information Laboratory on de-identification methods, including synthetic data generation, and the application of machine learning and generative AI to health data.

Lisa received her MD in virology and immunobiology from the University of Wuerzburg, Germany, in 2020. As a researcher at the Goethe University in Frankfurt/Main, Germany, she contributed to establishing national and international collaborative research platforms during the COVID-19 pandemic. She subsequently trained clinically in the Department of Nephrology and Medical Intensive Care at Charité – Universitätsmedizin Berlin.

Her work centers on translating real-world healthcare data into actionable insights using AI-based methods to support clinical research and decision-making.

Dr. Khaled El Emam is the Canada Research Chair (Tier 1) in Medical AI at the University of Ottawa, where he is a Professor in the School of Epidemiology and Public Health and Director of Medical AI at the Faculty of Medicine. He is also a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and Director of the multi-disciplinary Electronic Health Information Laboratory, conducting research on privacy enhancing technologies to enable the sharing of health data for secondary purposes, and methodology and applied machine learning on health data. Khaled also recently completed a one-year term as Scholar-in-Residence at the Office of the Information and Privacy Commissioner of Ontario (IPC).

Khaled has founded or co-founded eight product and services companies involved with data management and data analytics, with some having successful exits. Prior to his academic roles, he was a Senior Research Officer at the National Research Council of Canada. He also served as the head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany.

He participates in a number of committees including the European Medicines Agency Technical Anonymization Group, the Panel on Research Ethics advising on the TCPS, the Strategic Advisory Council of the Office of the Information and Privacy Commissioner of Ontario, and he is also co-editor-in-chief of the JMIR AI journal.

In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement. He held the Canada Research Chair in Electronic Health Information at the University of Ottawa from 2005 to 2015. Khaled has a PhD from the Department of Electrical and Electronics Engineering, King’s College, at the University of London, England.