An Attack on the Privacy of Sanitized Data that Fuses the Outputs of Multiple Data Miners

Abstract

Data sanitization has been used to restrict re-identification of individuals and disclosure of sensitive information from published data. We propose an attack on the privacy of the published sanitized data that simply fuses outputs of multiple data miners that are applied to the sanitized data. That attack is practical and does not require any background or additional information. We use a number of experiments to show scenarios where an adversary can combine outputs of multiple miners using a simple fusion strategy to increase their success chance of breaching privacy of individuals whose data is stored in the database. The fusion attack provides a powerful method of breaching privacy in the form of partial disclosure, for both anonymized and perturbed data. It also provides an effective way of approximating predictions of the best miner (a miner that provides the best results among all considered miners) when this miner cannot be determined.