Privacy-Preserving Frequent Itemset Mining in Outsourced Transaction Databases

Cloud computing has ushered a new interest in a paradigm called Datamining-as-a-service. This paradigm is aimed at organizations that lack the technical expertise or the computational resources enabling them to outsource their data mining tasks to a third party service provider. One of the main issues in this regard is the confidentiality of the valuable data at the server which the data owner considers as private. In this work, we study the problem of privacy preserving frequent itemset mining in outsourced transaction databases. We propose a novel hybrid method to achieve k-support anonymity based on statistical observations on the datasets. Our comprehensive experiments on real as well as synthetic datasets show that our techniques are effective and provide moderate privacy.