Anonymization of electronic medical records for validating genome-wide association studies
By Grigorios Loukides, Aris Gkoulalas-Divanis, Bradley A. Malin. 2010
137 citations recorded by the source corpus.
Subfields: Genetics
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In Genetics
Citations in the corpus
- Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, neutral): “[34] show that it is possible to identify a wide range of sensitive patient information from de-identified clinical data presented in a form standard among medical researchers, and later proposed a domain-specific utilitypreserving scheme similar to k-anonymity for mitigating these breaches [35].”
- Machine learning and genomics: precision medicine versus patient privacy (2018, methodology): “Nevertheless, recent work [40, 41, 42] has shown how to implement differential privacy for GWAS, making it possible for users to query a genetic database for the differentially private top k most associated SNPs.”
- Privacy-Preserving Data Exploration in Genome-Wide Association Studies (2013, methodology): “Differential privacy offers an approach to the problem that provides a rigorous privacy guarantee without unrealistic assumptions about the adversary’s knowledge (in contrast to other approaches [18]).”
- Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, methodology): “Common privacy-preserving methods include disclosure control [162,163], output perturbation [164,165] and anonymization [166,167].”
- Privacy-Preserving Data Sharing for Genome-Wide Association Studies (2012, background): “Others have criticized Homer et al., suggested alternative formulations of the identification problem, claimed to strengthen the attack or suggested different ways to protect the data, e.g., see [6, 13, 14, 15, 17, 18, 20, 22, 26].”
- New Models for Large Prospective Studies: Is There a Better Way? (2012, unknown): “Minimizing reidentification risk is an area of active investigation (27).”