Faculty, Staff and Student Publications

Authors

    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

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