PrivBasis

Ninghui Li, Wahbeh Qardaji, Dong Su, Jianneng Cao
2012 Proceedings of the VLDB Endowment  
The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a novel notion called basis sets. A θ-basis set has the property that any itemset with frequency higher than θ is a subset of some
more » ... . We introduce algorithms for privately constructing a basis set and then using it to find the most frequent itemsets. Experiments show that our approach greatly outperforms the current state of the art. Definition 1 (ǫ-Differential Privacy [16, 17] ). A randomized mechanism A gives ǫ-differential privacy if for any pair of neighboring datasets D and D ′ , and any S ∈ Range(A), Pr [A(D) = S] ≤ e ǫ · Pr A(D ′ ) = S .
doi:10.14778/2350229.2350251 fatcat:jfhgyho54nc3vkgr2kmr3jqloa