Authors:
Harun Gökçe
and
Osman Abul
Affiliation:
TOBB University of Economics and Technology, Turkey
Keyword(s):
Data mining, Frequent itemset mining, Privacy, Sensitive knowledge hiding.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data and Application Security and Privacy
;
Foundations of Knowledge Discovery in Databases
;
Information and Systems Security
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Privacy
;
Symbolic Systems
Abstract:
Sensitive frequent itemset hiding problem is typically solved by applying a sanitization process which transforms the source database into a release version. The main challenge in the process is to preserve the database utility while ensuring no sensitive knowledge is disclosed, directly or indirectly. Several algorithmic solutions based on different approaches are proposed to solve the problem. We observe that the available algorithms are like seesaws as far as both effectiveness and efficiency performances are considered. However, most practical domains demand for solutions with satisfactory effectiveness/efficiency performances, i.e., solutions balancing the tradeoff between the two. Motivated from this observation, in this paper, we present yet a simple and practical frequent itemset hiding algorithm targeting the balanced solutions. Experimental evaluation, on two datasets, shows that the algorithm indeed achieves a good balance between the two performance criteria.