Utility-Aware Data Anonymization Model for Healthcare Information

Fadi Alhaddadin; Jairo  Gutierrez

doi:10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372

Utility-Aware Data Anonymization Model for Healthcare Information

Fadi Alhaddadin, Jairo Gutierrez^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients' information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients' health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients' information before releasing it for research purposes according to certain requirements specified by the data user (researcher).

Original language	English
Title of host publication	Utility-Aware Data Anonymization Model for Healthcare Information
Place of Publication	Haikou, China
Publisher	IEEE
ISBN (Electronic)	9798350346558
ISBN (Print)	9798350346565
DOIs	https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372
Publication status	Published - 27 Jul 2023
Event	19th IEEE International Conference on Ubiquitous Intelligence and Computing 2022 - Haikou, China Duration: 15 Dec 2022 → 18 Dec 2022

Conference

Conference	19th IEEE International Conference on Ubiquitous Intelligence and Computing 2022
Abbreviated title	UIC 2022
Country/Territory	China
City	Haikou
Period	15/12/22 → 18/12/22

Keywords

Data privacy
Medical services
Privacy breach
Data models
Information filtering
Information integrity
Anonymization
Patients Records
Information Sharing
Data Privacy
Collaboration
l-diversity
(c,l)-diversity

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10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372

Cite this

@inproceedings{6f8df09be9b6435a87ab3280f04b3198,

title = "Utility-Aware Data Anonymization Model for Healthcare Information",

abstract = "The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients' information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients' health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients' information before releasing it for research purposes according to certain requirements specified by the data user (researcher).",

keywords = "Data privacy, Medical services, Privacy breach, Data models, Information filtering, Information integrity, Anonymization, Patients Records, Information Sharing, Data Privacy, Collaboration, l-diversity, (c,l)-diversity",

author = "Fadi Alhaddadin and Jairo Gutierrez",

year = "2023",

month = jul,

day = "27",

doi = "10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372",

language = "English",

isbn = "9798350346565",

booktitle = "Utility-Aware Data Anonymization Model for Healthcare Information",

publisher = "IEEE",

address = "United States",

note = "19th IEEE International Conference on Ubiquitous Intelligence and Computing 2022, UIC 2022 ; Conference date: 15-12-2022 Through 18-12-2022",

}

Alhaddadin, F & Gutierrez, J 2023, Utility-Aware Data Anonymization Model for Healthcare Information. in Utility-Aware Data Anonymization Model for Healthcare Information., 10189510, IEEE, Haikou, China, 19th IEEE International Conference on Ubiquitous Intelligence and Computing 2022, Haikou, China, 15/12/22. https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372

TY - GEN

T1 - Utility-Aware Data Anonymization Model for Healthcare Information

AU - Alhaddadin, Fadi

AU - Gutierrez, Jairo

PY - 2023/7/27

Y1 - 2023/7/27

N2 - The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients' information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients' health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients' information before releasing it for research purposes according to certain requirements specified by the data user (researcher).

AB - The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients' information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients' health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients' information before releasing it for research purposes according to certain requirements specified by the data user (researcher).

KW - Data privacy

KW - Medical services

KW - Privacy breach

KW - Data models

KW - Information filtering

KW - Information integrity

KW - Anonymization

KW - Patients Records

KW - Information Sharing

KW - Data Privacy

KW - Collaboration

KW - l-diversity

KW - (c,l)-diversity

U2 - 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372

DO - 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372

M3 - Conference contribution

SN - 9798350346565

BT - Utility-Aware Data Anonymization Model for Healthcare Information

PB - IEEE

CY - Haikou, China

T2 - 19th IEEE International Conference on Ubiquitous Intelligence and Computing 2022

Y2 - 15 December 2022 through 18 December 2022

ER -

Utility-Aware Data Anonymization Model for Healthcare Information

Abstract

Conference

Keywords

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