Privacy Preserving Data Mining: Case of Association Rules
Data mining has become an important technology to discover a hidden and nontrivial knowledge from large amounts of data. A major problem is to achieve this discovery process with preserving privacy of extracted data and / or knowledge. Privacy preserving data mining (PPDM) is a new area of research that studies the side effects of knowledge mining methods on individuals and organizations privacy. We present in this paper a state of the art of the PPDM in the case of association rules. We propose taxonomy of existing techniques and a classification of work realized in this context. This synthesis is followed by a discussion of certain issues and perspectives.
Keywords: Privacy, data hiding, rule hiding, association rules.
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ABOUT THE AUTHORS
Sarra Gacem
Computer Science department, Laboratory Signals, Systems and Data LSSD, University of Science and Technology of Oran Mohamed Boudiaf USTO-MB ALGERIA
Djamila Mokeddem
Computer Science department, Laboratory Signals, Systems and Data LSSD, University of Science and Technology of Oran Mohamed Boudiaf USTO-MB ALGERIA
Hafida Belbachir
Computer Science department, Laboratory Signals, Systems and Data LSSD, University of Science and Technology of Oran Mohamed Boudiaf USTO-MB ALGERIA
Sarra Gacem
Computer Science department, Laboratory Signals, Systems and Data LSSD, University of Science and Technology of Oran Mohamed Boudiaf USTO-MB ALGERIA
Djamila Mokeddem
Computer Science department, Laboratory Signals, Systems and Data LSSD, University of Science and Technology of Oran Mohamed Boudiaf USTO-MB ALGERIA
Hafida Belbachir
Computer Science department, Laboratory Signals, Systems and Data LSSD, University of Science and Technology of Oran Mohamed Boudiaf USTO-MB ALGERIA
