Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis

• URL: http://arxiv.org/abs/2010.14445v1
• Date: Tue, 27 Oct 2020 17:03:28 GMT
• Title: Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis
• Authors: James Scheibner, Jean Louis Raisaro, Juan Ram\'on Troncoso-Pastoriza, Marcello Ienca, Jacques Fellay, Effy Vayena, Jean-Pierre Hubaux
• Abstract summary: Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE) PETs provide a mathematical guarantee of privacy, with MHE providing performance advantage over separately using HE or SMC. We explain how MHE can reduce the reliance on customized contractual measures between institutions.
• Score: 5.6324529994086845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General Data Protection Regulation (GDPR) which has set a global benchmark for data protection. Specifically, the data processed and shared using MHE can be considered anonymized data. We explain how MHE can reduce the reliance on customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research whilst offering additional incentives for healthcare and research institutes to employ common data interoperability standards.

Related papers

• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease [0.0]
  This paper introduces a HIPAA compliant framework that can train from distributed data. I then propose a multimodal vertical federated model for Alzheimer's Disease (AD) detection. The VFL architecture proposed herein offers a novel distributed architecture, enabling collaborative learning across diverse sources of medical data.
  arXiv  Detail & Related papers  (2023-12-15T22:09:04Z)
• Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review [0.0]
  PPDM technique enables the extraction of actionable insight from enormous volume of data. Disclosing sensitive information infringes on patients' privacy. This paper aims to conduct a review of related work on privacy-preserving mechanisms, data protection regulations, and mitigating tactics.
  arXiv  Detail & Related papers  (2023-10-30T05:32:15Z)
• 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)
• MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation [110.31526448744096]
  We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
  arXiv  Detail & Related papers  (2021-09-29T18:09:41Z)
• Fidelity and Privacy of Synthetic Medical Data [0.0]
  The digitization of medical records ushered in a new era of big data to clinical science. The need to share individual-level medical data continues to grow, and has never been more urgent. enthusiasm for the use of big data has been tempered by a fully appropriate concern for patient autonomy and privacy.
  arXiv  Detail & Related papers  (2021-01-18T23:01:27Z)
• 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)
• Privacy and Data Balkanization: Circumventing the Barriers [0.0]
  Privacy concerns and laws are leading to significant overhead in arranging for sharing or combining different data sets. For new applications, where the benefit of combined data is not yet clear, this overhead can inhibit organizations from even trying to determine whether they can mutually benefit from sharing their data. We discuss techniques to overcome this difficulty by employing private information transfer to determine whether there is a benefit from sharing data, and whether there is room to negotiate acceptable prices.
  arXiv  Detail & Related papers  (2020-10-07T22:05:28Z)
• Secondary Use of Electronic Health Record: Opportunities and Challenges [0.0]
  Using EHR data for secondary purposes without consent creates privacy issues for individuals. Sharing of EHR across multiples agencies makes it vulnerable to cyber attacks. Data leak can cause financial losses or an individuals may encounter social boycott if their medical condition is exposed in public.
  arXiv  Detail & Related papers  (2020-01-26T16:22:53Z)

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.