Data mining tasks such as supervised classification can often benefit from a large training dataset. However, in many application domains, privacy concerns can hinder the construc...
We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on ra...
Data mining, with its promise to extract valuable, previously unknown and potentially useful patterns or knowledge from large data sets that contain private information is vulnerab...
Randomized Response techniques have been empirically investigated in privacy preserving association rule mining. However, previous research on privacy preserving market basket data...
Random perturbation is a promising technique for privacy preserving data mining. It retains an original sensitive value with a certain probability and replaces it with a random va...