Discovering frequent patterns in sensitive data

Abstract

Discovering frequent patterns from data is a popular exploratory technique in data mining. However, if the data are sensitive (e.g., patient health records, user behavior records) releasing information about significant patterns or trends carries significant risk to privacy. This paper shows how one can accurately discover and release the most significant patterns along with their frequencies in a data set containing sensitive information, while providing rigorous guarantees of privacy for the individuals whose information is stored there.We present two efficient algorithms for discovering the k most frequent patterns in a data set of sensitive records. Our algorithms satisfy differential privacy, a recently introduced definition that provides meaningful privacy guarantees in the presence of arbitrary external information. Differentially private algorithms require a degree of uncertainty in their output to preserve privacy. Our algorithms handle this by returning 'noisy' lists of patterns that are close to the actual list of k most frequent patterns in the data. We define a new notion of utility that quantifies the output accuracy of private top-k pattern mining algorithms. In typical data sets, our utility criterion implies low false positive and false negative rates in the reported lists. We prove that our methods meet the new utility criterion; we also demonstrate the performance of our algorithms through extensive experiments on the transaction data sets from the FIMI repository. While the paper focuses on frequent pattern mining, the techniques developed here are relevant whenever the data mining output is a list of elements ordered according to an appropriately 'robust' measure of interest.

An improved Apriori algorithm for mining association rules

“…Although the performance of classical Apriori algorithm cannot compete with that of state-of-the-art depth-first approaches [4][5][6][7], Apriori algorithm is always regarded as an important association rule mining algorithm because its basic idea of finding all frequent itemsets in a given database is universal and easy to implement for any association rules mining problems though the depth-first approaches suffers not only from the complexity of constructing FP-tree but also from the consumption of storage for recording nodes as well.…”

Differentially Private Utility Item Mining

“…An item set that found in Transaction often than minimum support threshold is subset of some basis with differential privacy guarantee. But [6] [13] [14] addresses some issues performing frequent item mining with differential privacy.…”

Differential Privacy with Bounded Priors

“…Algorithm 1 isdifferentially private [3,22]. Note that as the number of output SNPs M grows large, the sampling probabilities tend to be uniform.…”