Khaled El Emam

Senior Scientist, CHEO Research Institute Professor, Faculty of Medicine, University of Ottawa

Dr. Khaled El Emam is a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and head of the multi-disciplinary Electronic Health Information Laboratory, conducting research on privacy enhancing technologies to enable the sharing of health data for secondary purposes, including de-identification methods and synthetic data generation. He is also a Professor in the Faculty of Medicine at the University of Ottawa.

As an entrepreneur, Khaled founded or co-founded six companies involved with data management and data analytics. 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.

Previously, Khaled 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.

Khaled held the Canada Research Chair in Electronic Health Information at the University of Ottawa from 2005 to 2015. He has a PhD from the Department of Electrical and Electronics Engineering, King’s College, at the University of London, England.

Related News

Research Projects

  1. Evaluating the utility of synthetic COVID-19 case data


    A privacy risk assessment on the synthetic data showed that the attribute and membership disclosure risks were low.

  2. Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation


    We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data.

  3. Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles


    The application of AI algorithms in radiology requires access to large data sets containing PHI. The CAR AI Ethical and Legal standing committee published Part 2 of this guide to provide a practical approach to de-identification in the context of the current Canadian health care landscape. This article discussed the practical application of protecting patient data in the reality of our current Canadian clinical landscape. The strengths and weaknesses of de-identification approaches were outlined, along with the complexities of protecting patients’ medical imaging data, the possible de-identification software tools available, some common mistakes made by research and development teams, and perspectives on future directions.

  4. Practical Synthetic Data Generation


    Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.

  5. Building an Anonymization Pipeline


    How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.