Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

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• 作者：Kassaye Yitbarek Yigzaw ; Antonis Michalas…
• 关键词：Bloom Filter ; Data Reuse ; Deduplication ; Distributed Statistical Computation ; Data Linkage ; Duplicate Record ; Electronic Health Record ; Privacy ; Record Linkage ; Set Intersection
• 刊名：BMC Medical Informatics and Decision Making
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：17
• 期：1
• 全文大小：1776KB
• 刊物主题：Health Informatics; Information Systems and Communication Service; Management of Computing and Information Systems;
• 出版者：BioMed Central
• ISSN：1472-6947
• 卷排序：17

文摘

BackgroundTechniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step.