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A Decentralized, Privacy-preserving and Crowdsourcing-based Approach to Medical Research

机译:一种分散,隐私保留和基于众包的医学研究方法

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Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients’ data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers.
机译:在大尺度上访问数据加快了在医疗领域的研究进展。然而,对患者数据的可访问性面临着对监管,组织和技术水平的重大挑战。鉴于此,我们提出了一种基于众包范例的新方法来解决这一数据稀缺问题。利用BlockChain提供的基础设施,我们的分散性平台使研究人员能够从大量志愿者征求他们定义明确的研究学习的贡献。此外,为了克服违反隐私和相互信任的挑战,我们雇用了知识零知识论证的加密原语(ZK-SNARK)。这不仅允许参与者在不公开其隐私敏感的健康数据的情况下做出贡献,而且还提供了一种用户的分布式网络的手段,以便以有效的方式验证贡献的有效性。最后,由于没有激励机制到位,众包平台将无效,我们纳入智能合同,以确保患者和研究人员之间的奖励交换公平的互惠数据。

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