The Gradient of Health Data Privacy

• URL: http://arxiv.org/abs/2410.00897v1
• Date: Tue, 1 Oct 2024 17:35:18 GMT
• Title: The Gradient of Health Data Privacy
• Authors: Baihan Lin,
• Abstract summary: This paper introduces a novel "privacy gradient" approach to health data governance. Our multidimensional concept considers factors such as data sensitivity, stakeholder relationships, purpose of use, and temporal aspects. We demonstrate how this approach can address critical privacy challenges in diverse healthcare settings worldwide.
• Score: 15.417809900388262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of digital health and artificial intelligence, the management of patient data privacy has become increasingly complex, with significant implications for global health equity and patient trust. This paper introduces a novel "privacy gradient" approach to health data governance, offering a more nuanced and adaptive framework than traditional binary privacy models. Our multidimensional concept considers factors such as data sensitivity, stakeholder relationships, purpose of use, and temporal aspects, allowing for context-sensitive privacy protections. Through policy analyses, ethical considerations, and case studies spanning adolescent health, integrated care, and genomic research, we demonstrate how this approach can address critical privacy challenges in diverse healthcare settings worldwide. The privacy gradient model has the potential to enhance patient engagement, improve care coordination, and accelerate medical research while safeguarding individual privacy rights. We provide policy recommendations for implementing this approach, considering its impact on healthcare systems, research infrastructures, and global health initiatives. This work aims to inform policymakers, healthcare leaders, and digital health innovators, contributing to a more equitable, trustworthy, and effective global health data ecosystem in the digital age.

Related papers

• Privacy Risks in Health Big Data: A Systematic Literature Review [0.0]
  This paper outlines the key research in the field of health big data security. By analyzing existing research, this paper explores how cutting-edge technologies can enhance data security while protecting personal privacy.
  arXiv  Detail & Related papers  (2025-02-06T06:44:36Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Implications of Artificial Intelligence on Health Data Privacy and Confidentiality [0.0]
  The rapid integration of artificial intelligence in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency. However, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance. This paper examines the dual impact of AI on healthcare, highlighting its transformative potential and the critical need for safeguarding sensitive health information.
  arXiv  Detail & Related papers  (2025-01-03T05:17:23Z)
• Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision [2.7968600664591983]
  This paper presents a privacy-preserving framework for genomic research, developed in collaboration with Lynx.MD. The framework addresses critical cybersecurity and privacy challenges, enabling the privacy-preserving sharing and analysis of genomic data. Implementing the framework within Lynx.MD involves encoding genomic data into binary formats and applying noise through controlled perturbation techniques.
  arXiv  Detail & Related papers  (2024-07-12T05:43:13Z)
• A Qualitative Analysis Framework for mHealth Privacy Practices [0.0]
  This paper introduces a novel framework for the qualitative evaluation of privacy practices in mHealth apps. Our investigation encompasses an analysis of 152 leading mHealth apps on the Android platform. Our findings indicate persistent issues with negligence and misuse of sensitive user information.
  arXiv  Detail & Related papers  (2024-05-28T08:57:52Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• The Design and Implementation of a National AI Platform for Public Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
  The Italian National Health Service is adopting Artificial Intelligence through its technical agencies. Such a vast programme requires special care in formalising the knowledge domain. Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
  arXiv  Detail & Related papers  (2023-04-24T08:00:02Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)
• Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy [6.911121051195788]
  This paper explores the regulations, ethical guidelines around the world, and domain-specific needs. It presents the requirements and investigates the associated challenges. It illustrates the best available techniques for precision health data security and privacy with a conceptual system model.
  arXiv  Detail & Related papers  (2020-08-24T22:17:32Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.