Department of Computer Science
Towards accurate histogram publication under differential privacy
Histograms arc the workhorse of data mining and analysis. This paper considers the problem of publishing histograms under differential privacy, one of the strongest privacy models. Existing differentially private histogram publication schemes have shown that clustering (or grouping) is a promising idea to improve the accuracy of sanitized histograms. However, none of them fully exploits the benefit of clustering. In this paper, we introduce a new clustering framework. It features a sophisticated evaluation of the trade-off between the approximation error due to clustering and the Laplace error due to Laplace noise injected, which is normally overlooked in prior work. In particular, we propose three clustering strategies with different orders of run-time complexitics. We prove the superiority of our approach by theoretical utility comparisons with the competitors. Our extensive experiments over various standard real-life and synthetic datasets confirm that our technique consistently outperforms existing competitors.
Source Publication Title
Proceedings of the 2014 SIAM International Conference on Data Mining
Philadelphia, United States
Link to Publisher's Edition
Zhang, X., Ghent, R., Xu, J., Meng, X., & Xie, Y. (2014). Towards accurate histogram publication under differential privacy. Proceedings of the 2014 SIAM International Conference on Data Mining, 587-595. https://doi.org/10.1137/1.9781611973440.68