Privacy-preserving medical reports publishing for cluster analysis

Author First name, Last name, Institution

Ali Hmood, Concordia University
Benjamin C.M. Fung, McGill University
Farkhund Iqbal, Zayed University

Document Type

Conference Proceeding

Source of Publication

2014 6th International Conference on New Technologies, Mobility and Security - Proceedings of NTMS 2014 Conference and Workshops

Publication Date

1-1-2014

Abstract

Health data mining is an emerging research direction. High-quality health data mining results rely on having access to high-quality patient information. Yet, releasing patient-specific medical reports may potentially reveal sensitive information of the individual patients. In this paper, we study the problem of anonymizing medical reports and present a solution to anonymize a collection of medical reports while preserving the information utility of the medical reports for the purpose of cluster analysis. Experimental results show that our proposed approach can the impact of anonymization on the cluster quality is minor, suggesting that the feasibility of simultaneously preserving both information utility and privacy in anonymous medical reports. © 2014 IEEE.

DOI Link

10.1109/ntms.2014.6814045

Publisher

IEEE Computer Society

Last Page

8

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

anonymity, healthcare, Privacy, text clustering

Scopus ID

84901414300

Recommended Citation

Hmood, Ali; Fung, Benjamin C.M.; and Iqbal, Farkhund, "Privacy-preserving medical reports publishing for cluster analysis" (2014). All Works. 2802.
https://zuscholars.zu.ac.ae/works/2802

Indexed in Scopus

yes

Open Access

no