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

Healthcare Technology Letters

article

Aristos Aristodimou¹  Athos Antoniades¹  Constantinos S. Pattichis¹ 

[1] Department of Computer Science, University of Cyprus

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;   

DOI  :  10.1049/htl.2015.0050

学科分类：肠胃与肝脏病学

来源: Wiley

【 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.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND