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

This paper is focused 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 (quasiidentifier) 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.