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

By Aris Gkoulalas-Divanis, Grigorios Loukides

Anonymization of digital clinical documents to aid medical research heavily examines the privateness threats which could come up from clinical facts sharing, and surveys the cutting-edge equipment constructed to protect info opposed to those threats.

To encourage the necessity for computational equipment, the e-book first explores the most demanding situations dealing with the privacy-protection of clinical information utilizing the prevailing rules, practices and rules. Then, it takes an in-depth examine the preferred computational privacy-preserving tools which have been built for demographic, scientific and genomic information sharing, and heavily analyzes the privateness ideas at the back of those equipment, in addition to the optimization and algorithmic ideas that they hire. eventually, via a sequence of in-depth case stories that spotlight info from the U.S. Census in addition to the Vanderbilt collage scientific heart, the e-book outlines a brand new, cutting edge classification of privacy-preserving equipment designed to make sure the integrity of transferred scientific facts for next research, equivalent to getting to know or validating institutions among medical and genomic info.

Anonymization of digital clinical documents to help medical research is meant for execs as a reference advisor for protecting the privateness and knowledge integrity of delicate scientific documents. teachers and different learn scientists also will locate the ebook invaluable.

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In: ICDE, p. 24 (2006) 48. : Identifiability in biobanks: models, measures, and mitigation strategies. Human Genetics 130(3), 383–392 (2011) 49. : On the complexity of optimal k-anonymity. In: PODS, pp. 223–228 (2004) 50. National Institutes of Health: Policy for sharing of data obtained in NIH supported or conducted genome-wide association studies. NOT-OD-07-088. 2007. 51. : Thoughts on k-anonymization. DKE 63(3), 622–645 (2007) 52. : Effects of data anonymization by cell suppression on descriptive statistics and predictive modeling performance.

Update D˜ based on C 8. Create subpartitions of D˜ such that each of them contains all transactions in D˜ that have exactly the same generalized items 9. Balance the subpartitions so that each of them has at least k transactions 10. for each subpartition D˜ 11. Execute Partition(D˜ , C , H , k) a generalization hierarchy H , and a parameter k. D˜ initially contains a single generalized item that appears in the root of the generalization hierarchy H and replaces all items. , a set of nodes in H , such that every item in the domain I can be replaced by exactly one node in the set, according to the hierarchy-based generalization model.

In: Secure Data Management, pp. 124–141 (2007) 64. S. Department of Health and Human Services Office for Civil Rights: HIPAA administrative simplification regulation text (2006) 65. : Learning your identity and disease from research papers: information leaks in genome wide association study. In: CCS, pp. 534–544 (2009) 66. Wang: alpha-k-anonymity: An enhanced k-anonymity model for privacy-preserving data publishing. In: KDD, pp. 754–759 (2006) 67. : Personalized privacy preservation. In: SIGMOD, pp.

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