Khaled El Emam

Khaled El Emam

Dr. Khaled El Emam is the Founder and CEO of Privacy Analytics, Inc. He is also an Associate Professor at the University of Ottawa, Faculty of Medicine, a senior investigator at the Children's Hospital of Eastern Ontario Research Institute, and a Canada Research Chair in Electronic Health Information at the University of Ottawa. His main area of research is developing techniques for health data de-identification or anonymization and secure disease surveillance for public health purposes. He has made many contributions to the health privacy area. In addition, he has considerable experience de-identifying personal health information under the HIPAA Privacy Rule Statistical Standard.

Previously Khaled was a Senior Research Officer at the National Research Council of Canada, and prior to that he was head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. He has co-founded two companies to commercialize the results of his research work. 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, and ranked second in 2002 and 2005. He holds a Ph.D. from the Department of Electrical and Electronic Engineering, King's College, at the University of London (UK). His website is www.ehealthinformation.ca

Anonymizing Health Data Anonymizing Health Data
by Luk Arbuckle, Khaled El Emam
December 2013
Print: $29.99
Ebook: $25.99

Webcast: Anonymizing Health Data
September 13, 2013
In this webcast we'll start with a discussion of the relatively simple de-identification of a cross-sectional disease registry, and then we'll jump in to more complex situations like the de-identification of longitudinal data, free-form text, and geospatial...

Webcast: Responsibly Sharing Data Under HIPAA
October 31, 2012
In this webcast presentation we will first provide an overview of how data can be re-identified, with reference to a number of recent real world examples. This will be followed by a description of how to de-identify health data in a defensible way according...