Privacy preservation in releasing patient data

Xiaoping Liu; Xiao Bai Li; Luvai Motiwalla; Wenjun Li; Hua Zheng; Patricia Durkin Franklin

Privacy preservation in releasing patient data

Xiaoping Liu, Xiao Bai Li, Luvai Motiwalla, Wenjun Li, Hua Zheng, Patricia Durkin Franklin

Medical Social Sciences

Research output: Contribution to conference › Paper › peer-review

Abstract

When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for this problem and identify appropriate risk measures. An efficient algorithm is developed for anonymizing the data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.

Original language	English (US)
State	Published - 2016
Event	22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 - San Diego, United States Duration: Aug 11 2016 → Aug 14 2016

Other

Other	22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016
Country/Territory	United States
City	San Diego
Period	8/11/16 → 8/14/16

Keywords

Patient data
Privacy
Risk

ASJC Scopus subject areas

Computer Science Applications
Computer Networks and Communications
Information Systems

Cite this

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title = "Privacy preservation in releasing patient data",

abstract = "When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for this problem and identify appropriate risk measures. An efficient algorithm is developed for anonymizing the data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.",

keywords = "Patient data, Privacy, Risk",

author = "Xiaoping Liu and Li, {Xiao Bai} and Luvai Motiwalla and Wenjun Li and Hua Zheng and Franklin, {Patricia Durkin}",

note = "Funding Information: This research was supported by the National Library of Medicine (NLM) of the National Institutes of Health (NIH) under Grant Numbers R01LM010942. The content is solely the responsibility of the authors and does not necessarily represent the official views of NLM or NIH.; 22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 ; Conference date: 11-08-2016 Through 14-08-2016",

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T1 - Privacy preservation in releasing patient data

AU - Liu, Xiaoping

AU - Li, Xiao Bai

AU - Motiwalla, Luvai

AU - Li, Wenjun

AU - Zheng, Hua

AU - Franklin, Patricia Durkin

N1 - Funding Information: This research was supported by the National Library of Medicine (NLM) of the National Institutes of Health (NIH) under Grant Numbers R01LM010942. The content is solely the responsibility of the authors and does not necessarily represent the official views of NLM or NIH.

PY - 2016

Y1 - 2016

N2 - When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for this problem and identify appropriate risk measures. An efficient algorithm is developed for anonymizing the data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.

AB - When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for this problem and identify appropriate risk measures. An efficient algorithm is developed for anonymizing the data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.

KW - Patient data

KW - Privacy

KW - Risk

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M3 - Paper

AN - SCOPUS:84987678238

T2 - 22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016

Y2 - 11 August 2016 through 14 August 2016

ER -

Privacy preservation in releasing patient data

Abstract

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Keywords

ASJC Scopus subject areas

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