Full Citation
Title: Efficient and effective pruning strategies for health data de-identification
Citation Type: Journal Article
Publication Year: 2016
ISBN:
ISSN:
DOI: 10.1186/s12911-016-0287-2
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PMCID:
PMID:
Abstract: Background Privacy must be protected when sensitive biomedical data is shared, e.g. for research purposes. Data de-identification is an important safeguard, where datasets are transformed to meet two conflicting objectives: minimizing re-identification risks while maximizing data quality. Typically, de-identification methods search a solution space of possible data transformations to find a good solution to a given de-identification problem. In this process, parts of the search space must be excluded to maintain scalability. Objectives The set of transformations which are solution candidates is typically narrowed down by storing the results obtained during the search process and then using them to predict properties of the output of other transformations in terms of privacy (first objective) and data quality (second objective). However, due to the exponential growth of the size of the search space, previous implementations of this method are not well-suited when datasets contain many attributes which need to be protected. As this is often the case with biomedical research data, e.g. as a result of longitudinal collection, we have developed a novel method. Methods Our approach combines the mathematical concept of antichains with a data structure inspired by prefix trees to represent properties of a large number of data transformations while requiring only a minimal amount of information to be stored. To analyze the improvements which can be achieved by adopting our method, we have integrated it into an existing algorithm and we have also implemented a simple best-first branch and bound search (BFS) algorithm as a first step towards methods which fully exploit our approach. We have evaluated these implementations with several real-world datasets and the k-anonymity privacy model. Results When integrated into existing de-identification algorithms for low-dimensional data, our . . .
Url: https://link.springer.com/article/10.1186/s12911-016-0287-2
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Authors: Prasser, Fabian; Kohlmayer, Florian; Kuhn, Klaus A.
Periodical (Full): BMC Medical Informatics and Decision Making
Issue: 1
Volume: 16
Pages: 1-14
Data Collections: IPUMS Time Use - ATUS, IPUMS Health Surveys - NHIS
Topics: Other, Population Health and Health Systems
Countries: