In an on-line statistical database, the query-answering system should prevent answers to statistical queries from leading to disclosure of confidential data. On the other hand, a statistical user is inclined to data mining, that is, to disclose pieces of information that are implicit in the (explicit) answers to his queries. A key task for both is to find data that is derivable from given summary statistics. We show that this task is easy if data is additive and the set of given summary statistics can be modelled by a graph.
Malvestuto, F., Mezzini, M. (2004). Privacy Preserving and Data Mining in an On-Line Statistical Database of Additive Type. In CASC Project International Workshop, PSD 2004 (pp.353-365). Springer [10.1007/978-3-540-25955-8_29].
Privacy Preserving and Data Mining in an On-Line Statistical Database of Additive Type
MEZZINI, MAURO
2004-01-01
Abstract
In an on-line statistical database, the query-answering system should prevent answers to statistical queries from leading to disclosure of confidential data. On the other hand, a statistical user is inclined to data mining, that is, to disclose pieces of information that are implicit in the (explicit) answers to his queries. A key task for both is to find data that is derivable from given summary statistics. We show that this task is easy if data is additive and the set of given summary statistics can be modelled by a graph.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
