Interpretability of fuzzy association rules as means of discovering threats to privacy

Abstract

This paper focuses on studying how data privacy could be preserved with fuzzy rule bases as interpretable as possible. These fuzzy rule bases are obtained from a data mining strategy based on building a decision tree. The antecedents of each rule produced by these systems contain information about the released variables (quasi-identifier), whereas the consequent contains information only about the protected variable. Experimental results show that fuzzy rules are generally simpler and easier to interpret than other approaches but the risk of disclosing does not increase.

Keywords:

• disclosure control
• fuzzy rules
• data privacy

2010 AMS Classifications :

• 68T01
• 68T27
• 68T35

Acknowledgements

The authors acknowledge the financial support by Grants MTM2008-01519 and TIN2010-14971 from Ministry of Science and Innovation and Grant TIN2007-61273 from Ministry of Education and Science, Government of Spain.