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Spherical microaggregation: Anonymizing sparse vector spaces

AuthorsAbril, Daniel; Navarro-Arribas, Guillermo; Torra, Vicenç
KeywordsData mining
High dimensional data
Statistical disclosure Control
Information loss
Privacy preserving
Clustering algorithms
Data privacy
Vector spaces
Issue Date2015
CitationComputers and Security 49: 28- 44 (2015)
AbstractUnstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets. © 2014 Elsevier Ltd. All rights reserved.
Identifiersdoi: 10.1016/j.cose.2014.11.005
issn: 0167-4048
uri: http://www.sciencedirect.com/science/article/pii/S0167404814001679
Appears in Collections:(IIIA) Artículos
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