期刊文章%@ 1438-8871 %I JMIR Publicatio卡塔尔世界杯8强波胆分析ns %V 21 %N 8 %P e13600 %T使用区块链进行特性工程的隐私保护方法:%A Jones,Michael %A Johnson,Matthew %A Shervey,Mark %A Dudley,Joel T %A Zimmerman,Noah %+生物医学研究中心，西奈山伊坎医学院，雷德伍德市马歇尔街10-234号，美国CA 94063, 1 650 352 3879，noah.zimmerman@mssm.edu %K隐私%K机器学习%K机密%K数据收集%K移动健康%K特征工程%K地理定位%K区块链%K智能合约%K加密%K可信执行环境%D 2019 %7 14.08.2019 %9原始论文%J J Med Internet Res %G英文%X背景:对从研究参与者收集可识别信息的研究来说，保护私人数据是一项关键责任。限制数据收集的范围和防止数据的二次使用是管理这些风险的有效策略。一个理想的数据收集框架应该包括特征工程，在一个没有可信第三方的安全环境中，从敏感的原始数据中提取次要特征的过程。目的:本研究旨在比较当前的方法，基于他们如何维护数据隐私和他们的实现的实用性。这些方法包括依赖可信第三方的传统方法，以及加密、安全硬件和基于区块链的技术。方法:定义一组属性来评估每种方法。在此基础上进行了定性比较。每种方法的评估都以共享生物医学研究地理位置数据的用例为框架。 Results: We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement. Conclusions: Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications. %M 31414666 %R 10.2196/13600 %U //www.mybigtv.com/2019/8/e13600/ %U https://doi.org/10.2196/13600 %U http://www.ncbi.nlm.nih.gov/pubmed/31414666