Dynamic Anonymous Index for Confidential Data
نویسندگان
چکیده
In this paper we introduce a k-anonymous vector space model, which can be used as an index of a set of confidential documents. This model allows to index, for example, encrypted data. New documents can be added or removed while maintaining the k-anonymity property of the vector space. URL http://dx.doi.org/10.1007/978-3-642-54568-9_23 [10] Source URL: https://www.iiia.csic.es/en/node/54461 Links [1] https://www.iiia.csic.es/en/staff/guillermo-navarro-arribas [2] https://www.iiia.csic.es/en/staff/daniel-abril [3] https://www.iiia.csic.es/en/staff/vicen%C3%A7-torra [4] https://www.iiia.csic.es/en/bibliography?f[author]=682 [5] https://www.iiia.csic.es/en/bibliography?f[author]=683 [6] https://www.iiia.csic.es/en/bibliography?f[author]=684 [7] https://www.iiia.csic.es/en/bibliography?f[author]=685 [8] https://www.iiia.csic.es/en/bibliography?f[author]=686 [9] https://www.iiia.csic.es/en/bibliography?f[keyword]=558 [10] http://dx.doi.org/10.1007/978-3-642-54568-9_23
منابع مشابه
Working Paper ENGLISH ONLY UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE (UNECE) CONFERENCE OF EUROPEAN STATISTICIANS EUROPEAN COMMISSION STATISTICAL OFFICE OF THE EUROPEAN
The usual approach to generate k-anonymous data sets, based on generalization of the quasi-identifier attributes, does not provide any control on the variability of the confidential attributes within the k-anonymous groups. If the latter variability is too small, privacy is not sufficiently protected, while, for large variabilities, data utility is substantially damaged. Some refinements to the...
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