Λεπτομέρειες

ΕίδοςΔημοσίευση
ΚωδικόςTR-2013-7
ΤίτλοςΑνωνυμοποίηση Παρουσία Δομικής Γνώσης
ΣυγγραφέαςΌλγα Γκουντούνα και Μανώλης Τερροβίτης
Έτος2013
Λέξεις κλειδιάprivacy, anonymity, structural knowledge
ΠερίληψηReal-world data usually have implicit or explicit structural relations. For instance, databases link records through foreign keys, and XML documents express associations between different values through syntax. Privacy preservation, until now, has focused either on data with a very simple structure, e.g. relational tables, or on data with very complex structure e.g. social network graphs, but has ignored intermediate cases, which are the most frequent in practice. In this work, we focus on tree structured data. Such data stem from various applications, even when the structure is not directly implied by the syntax, e.g. XML documents. A characteristic case is a database where information about a single person is scattered amongst different tables that are associated through foreign keys. We define k(m,n)-anonymity, which provides protection against identity disclosure and we propose a greedy anonymization heuristic that is able to sanitize large datasets. The algorithms and the quality of the anonymization are evaluated experimentally.
ΚατηγορίαGeneral DBMS
ΔημοσίευσηTechnical Report 2013
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