Privacy preserving data publishing of categorical data through k -anonymity and feature selection

[摘要] In healthcare, there is a vast amount of patients’ data, which can lead to important discoveries if combined. Due to legal and ethical issues, such data cannot be shared and hence such information is underused. A new area of research has emerged, called privacy preserving data publishing (PPDP), which aims in sharing data in a way that privacy is preserved while the information lost is kept at a minimum. In this Letter, a new anonymisation algorithm for PPDP is proposed, which is based on k -anonymity through pattern-based multidimensional suppression (kPB-MS). The algorithm uses feature selection for reducing the data dimensionality and then combines attribute and record suppression for obtaining k -anonymity. Five datasets from different areas of life sciences [RETINOPATHY, Single Proton Emission Computed Tomography imaging, gene sequencing and drug discovery (two datasets)], were anonymised with kPB-MS. The produced anonymised datasets were evaluated using four different classifiers and in 74% of the test cases, they produced similar or better accuracies than using the full datasets.

[学科分类] 肠胃与肝脏病学

[关键词] feature selection;single photon emission computed tomography;data privacy;drugs;medical computing;classifier;drug discovery;gene sequencing;SPECT imaging;single proton emission computed tomography;RETINOPATHY;data dimensionality;k-anonymity through pattern-based multidimensional suppression;anonymisation algorithm;data sharing;privacy preserving data publishing;feature selection;categorical data