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Secure attribute sharing of linked microdata

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PRE
AI
K
Krishnamurty Muralidhar *
R
Rathindra Sarathy *
李晗 封面图
李晗 (Han Li) *
DOI:10.1016/j.dss.2015.10.005delete
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摘要

摘要

En 中文
Two organizations that have records on the same collection of individuals can benefit from sharing attributes on these individuals. The combined data, with records linked on certain common identifying information,,is termed linked microdata. Linked microdata attributes can add considerable value to organizations by enabling them to perform analysis that can provide important information on individual (or record-level) data items. We illustrate practical examples of the need and benefits of sharing linked microdata and identify important privacy issues relating to this context. Based on a conditional distribution approach, we develop a procedure (SASH) for sharing masked attributes in linked microdata that addresses these privacy issues. Our experimental results show that SASH achieves a priori expectations of analytical usefulness, without either party having to provide true values of attribute data. Our results also show that an ad hoc approach such as data swapping, cannot achieve privacy without sacrificing usefulness or vice versa. Our study should provide immediate practical benefits to organizations interested in secure attribute sharing of linked microdata. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Secure attribute sharing
Microdata
Confidentiality
Privacy-preserving data sharing
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Decision Support Systems 封面图
Decision Support Systems
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