Publishing Set-Valued Data via Differential Privacy
Set-valued data provides enormous opportunities for various data mining tasks. In this paper, the authors study the problem of publishing set-valued data for data mining tasks under the rigorous differential privacy model. All existing data publishing methods for set-valued data are based on partition-based privacy models, for example k-anonymity, which are vulnerable to privacy attacks based on background knowledge. In contrast, differential privacy provides strong privacy guarantees independent of an adversary's background knowledge and computational power.