Differentially Private Set-Valued Data Release against Incremental Updates

Publication of the private set-valued data will provide enormous opportunities for counting queries and various data mining tasks. Compared to those previous methods based on partition-based privacy models (e.g., k-anonymity), differential privacy provides strong privacy guarantees against adversaries with arbitrary background knowledge. However, the existing solutions based on differential privacy for data publication are currently limited to static datasets, and do not adequately address today’s demand for up-to-date information. In this paper, we address the problem of differentially private set-valued data release on an incremental scenario in which the data need to be transformed are not static. Motivated by this, we propose an efficient algorithm, called IncTDPart, to incrementally generate a series of differentially private releases. The proposed algorithm is based on top-down partitioning model with the help of item-free taxonomy tree and update-bounded mechanism. Extensive experiments on real datasets confirm that our approach maintains high utility and scalability for counting query.