Open tools for quantitative anonymization of tabular phenotype data: literature review.

ABSTRACT: Precision medicine relies on molecular and systems biology methods as well as bidirectional association studies of phenotypes and (high-throughput) genomic data. However, the integrated use of such data often faces obstacles, especially in regards to data protection. An important prerequisite for research data processing is usually informed consent. But collecting consent is not always feasible, in particular when data are to be analyzed retrospectively. For phenotype data, anonymization, i.e. the altering of data in such a way that individuals cannot be identified, can provide an alternative. Several re-identification attacks have shown that this is a complex task and that simply removing directly identifying attributes such as names is usually not enough. More formal approaches are neede

SUBMITTER: Haber AC 

PROVIDER: S-EPMC9677485 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature