On Differentially Private Frequent Itemsets Mining

Frequent itemsets mining finds sets of items that frequently appear together in a database. However, publishing this information might have privacy implications. Accordingly, in this paper we are considering the problem of guaranteeing differential privacy for frequent itemsets mining. We measure the utility of a frequent itemsets mining algorithm by its likelihood to produce a complete and sound result where “completeness” requires the algorithm to include the “sufficiently” frequent itemsets and “soundness” needs the algorithm to exclude the“sufficiently” infrequent ones. We prove that it is hard to simultaneously satisfy both differential privacy and a non-trivial utility requirement. However, we find that we can produce reasonably accurate results while still guaranteeing differential privacy on benchmark datasets [19] by truncating transactions to limit their cardinality.