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

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), whi...

Full description

Saved in:
Bibliographic Details
Published in:Healthcare technology letters 2016-03, Vol.3 (1), p.16-21
Main Authors: Aristodimou, Aristos, Antoniades, Athos, Pattichis, Constantinos S
Format: Article
Language:English
Subjects:
anonymisation algorithm
categorical data
classifier
data dimensionality
data privacy
data sharing
drug discovery
drugs
feature selection
gene sequencing
k‐anonymity through pattern‐based multidimensional suppression
medical computing
privacy preserving data publishing
RETINOPATHY
single photon emission computed tomography
single proton emission computed tomography
Special Issue: Decision Support for Person-Centred Healthcare
SPECT imaging
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2053-3713
2053-3713
DOI:10.1049/htl.2015.0050