期刊
DATA & KNOWLEDGE ENGINEERING
卷 64, 期 1, 页码 294-311出版社
ELSEVIER
DOI: 10.1016/j.datak.2007.06.016
关键词
security & privacy; knowledge discovery; distributed databases
In the past, data holders protected the privacy of their constituents by issuing separate disclosures of sensitive (e.g., DNA) and identifying data (e.g., names). However, individuals visit many places and their location-visit patterns, or '' trails '', can re-identify seemingly anonymous data. In this paper, we introduce a formal model of privacy protection, called k-unlinkability, to prevent trail re-identification in distributed data. The model guarantees that sensitive data trails are linkable to no less than k identities. We develop a graph-based model and illustrate how k-unlinkability is a more appropriate solution to this privacy problem compared to alternative privacy protection models. (c) 2007 Elsevier B.V. All rights reserved.
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