Anonymization of Electronic Medical Records to Support by Aris Gkoulalas-Divanis, Grigorios Loukides 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 scientific research heavily examines the privateness threats that could come up from clinical info sharing, and surveys the state of the art equipment constructed to defend facts opposed to those threats.

To inspire the necessity for computational equipment, the ebook first explores the most demanding situations dealing with the privacy-protection of clinical information utilizing the present regulations, practices and laws. Then, it takes an in-depth examine the preferred computational privacy-preserving equipment which were built for demographic, medical and genomic information sharing, and heavily analyzes the privateness rules at the back of those equipment, in addition to the optimization and algorithmic concepts that they hire. ultimately, via a sequence of in-depth case experiences that spotlight facts from the USA Census in addition to the Vanderbilt college clinical middle, the e-book outlines a brand new, leading edge type of privacy-preserving tools designed to make sure the integrity of transferred scientific information for next research, akin to researching or validating institutions among scientific and genomic details.

Anonymization of digital clinical documents to help scientific research is meant for pros as a reference consultant for shielding the privateness and information integrity of delicate scientific documents. lecturers and different examine scientists also will locate the e-book invaluable.

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Additional info for Anonymization of Electronic Medical Records to Support Clinical Analysis

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First, it unnecessarily restricts the number of possible generalizations, which may harm data utility [42]. , “too” coarse) or non-existent [56]. He et al. [28] applied the hierarchy-based model in a local manner, allowing different occurrences of the same item to be replaced by different generalized items. In a different line of research, Xu et al. [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.

It is worth noting that this attack differs from the identity and semantic information disclosure attacks that were discussed in Sects. 2, respectively. In the attack we consider, the published dataset does not necessarily contain the records of all patients, whose information is contained in the EMR system, and that each published record contains a DNA sequence that is not contained in the identified EMR dataset. This is because, it is often the case that the data of a selected subset of patients are useful in the context of GWAS and that the patients’ DNA sequences are not accessible to all users of the EMR system [12].

28] applied the hierarchy-based model in a local manner, allowing different occurrences of the same item to be replaced by different generalized items. In a different line of research, Xu et al. [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.

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


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