Anonymization of Electronic Medical Records to Support Clinical
Analysis closely examines the privacy threats that may arise from
medical data sharing, and surveys the state-of-the-art methods
developed to safeguard data against these threats.
To motivate the need for computational methods, the book first
explores the main challenges facing the privacy-protection of
medical data using the existing policies, practices and
regulations. Then, it takes an in-depth look at the popular
computational privacy-preserving methods that have been developed
for demographic, clinical and genomic data sharing, and closely
analyzes the privacy principles behind these methods, as well as
the optimization and algorithmic strategies that they employ.
Finally, through a series of in-depth case studies that highlight
data from the US Census as well as the Vanderbilt University
Medical Center, the book outlines a new, innovative class of
privacy-preserving methods designed to ensure the integrity of
transferred medical data for subsequent analysis, such as
discovering or validating associations between clinical and genomic
information.
Anonymization of Electronic Medical Records to Support Clinical
Analysis is intended for professionals as a reference guide for
safeguarding the privacy and data integrity of sensitive medical
records. Academics and other research scientists will also find the
book invaluable.
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