Privacy preserving data publishing of categorical data through k‐anonymity and feature selection. Issue 1 (23rd March 2016)

• Record Type: Journal Article
• Authors: Aristodimou, Aristos; Antoniades, Athos; Pattichis, Constantinos S.
• Is Part Of: Healthcare technology letters. Volume 3:Issue 1(2016)
• Page Start: 16
• Page End: 21
• Publication Date: 2016-03-23
• Subjects: 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; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2015.0050
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : 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.