Aris Gkoulalas-Divanis, Grigorios Loukides's Anonymization of Electronic Medical Records to Support PDF

By Aris Gkoulalas-Divanis, Grigorios Loukides

ISBN-10: 1461456673

ISBN-13: 9781461456674

ISBN-10: 1461456681

ISBN-13: 9781461456681

Anonymization of digital scientific files to aid medical research heavily examines the privateness threats which may come up from clinical information sharing, and surveys the state of the art tools built to guard information opposed to those threats.

To encourage the necessity for computational tools, the ebook first explores the most demanding situations dealing with the privacy-protection of scientific information utilizing the prevailing guidelines, practices and laws. Then, it takes an in-depth examine the preferred computational privacy-preserving tools which have been built for demographic, medical and genomic info sharing, and heavily analyzes the privateness rules at the back of those tools, in addition to the optimization and algorithmic ideas that they hire. ultimately, via a chain of in-depth case experiences that spotlight facts from the united states Census in addition to the Vanderbilt college scientific middle, the booklet outlines a brand new, leading edge category of privacy-preserving tools designed to make sure the integrity of transferred clinical info for next research, corresponding to researching or validating institutions among medical and genomic details.

Anonymization of digital scientific files to help medical research is meant for execs as a reference advisor for shielding the privateness and knowledge integrity of delicate clinical documents. lecturers and different study scientists also will locate the booklet invaluable.

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36 3 Re-identification of Clinical Data Through Diagnosis Information Fig. 00, in the dataset of Fig. 4 Utility Measures The protection of patient privacy is a regulatory and ethical requirement, but, at the same time, the application of data transformation strategies that are required to achieve privacy may limit the scientific usefulness of the published data. Thus, it is important to measure the loss in utility when applying protection strategies. There are many data utility measures that have been proposed in the literature [1, 5, 14, 16] but are not applicable to our setting, because they either deal with generalized [1, 14, 16] or nominal data [1] only.

60] proposed applying global suppression to non-sensitive items, and pointed out that the latter operation has the important benefit of preserving the support of original non-suppressed items. Cao et al. [9] proposed a global suppression model that can be applied to both sensitive and not-sensitive items. Overall, generalization typically incurs a lower amount of information loss than suppression, and global generalization models are preferred due to their ability to preserve data utility in data analysis and mining applications.

4, we turn our attention to measures that capture the loss of utility entailed by anonymization when sharing patients records. 2 Structure of the Datasets Used in the Attack Following the notation that was presented in Chap. 2, we consider a dataset DP that contains |DP | transactions. , a patient’s name), and I is an itemset. I is comprised of diagnosis codes, which are derived from the domain I of ICD codes. For example, the dataset shown in Fig. 00}. Also, DS is a dataset that contains |DS | records of the form I, DNA .

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Anonymization of Electronic Medical Records to Support Clinical Analysis by Aris Gkoulalas-Divanis, Grigorios Loukides


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