Faculty, Staff and Student Publications
Language
English
Publication Date
10-1-2023
Journal
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
DOI
10.1145/3583780.3614864
PMID
37906633
PMCID
PMC10601092
PubMedCentral® Posted Date
10-21-2024
PubMedCentral® Full Text Version
Author MSS
Abstract
Sharing health data is vital in advancing medical research and transforming knowledge into clinical practice. Meanwhile, protecting the privacy of data contributors is of paramount importance. To that end, several privacy approaches have been proposed to protect individual data contributors in data sharing, including data anonymization and data synthesis techniques. These approaches have shown promising results in providing privacy protection at the dataset level. In this work, we study the privacy challenges in enabling fine-grained privacy in health data sharing. Our work is motivated by recent research findings, in which patients and healthcare providers may have different privacy preferences and policies that need to be addressed. Specifically, we propose a novel and effective privacy solution that enables data curators (e.g., healthcare providers) to protect sensitive data elements while preserving data usefulness. Our solution builds on randomized techniques to provide rigorous privacy protection for sensitive elements and leverages graphical models to mitigate privacy leakage due to dependent elements. To enhance the usefulness of the shared data, our randomized mechanism incorporates domain knowledge to preserve semantic similarity and adopts a block-structured design to minimize utility loss. Evaluations with real-world health data demonstrate the effectiveness of our approach and the usefulness of the shared data for health applications.
Keywords
Data Sharing, Data Privacy, Health Data
Published Open-Access
yes
Recommended Citation
Luca Bonomi, Sepand Gousheh, and Liyue Fan, "Enabling Health Data Sharing with Fine-Grained Privacy" (2023). Faculty, Staff and Student Publications. 894.
https://digitalcommons.library.tmc.edu/uthshis_docs/894