Discovering frequent patterns in sensitive data

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’ list...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Raghav Bhaskar, Srivatsan Laxman, Adam Smith, Abhradeep Thakurta
• Keywords: Algorithms, Data Mining, Data Sets, Frequent Pattern, KDD 2010