Towards accurate histogram publication under differential privacy.

Abstract Histograms are 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 complexities. 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

Unsupervised Saliency Estimation based on Robust Hypotheses

Visual saliency estimation based on optimization models is gaining increasing popularity recently. In this paper, we formulate saliency estimation as a quadratic program (QP) problem based on robust hypotheses. First, we propose an adaptive center-based bias hypothesis to replace the most common image center-based center-bias. It calculates the weighted center by utilizing local contrast which is much more robust when the objects are far away from the image center. Second, we model smoothness term on saliency statistics of each color. It forces the pixels with similar colors to have similar saliency statistics. The proposed smoothness term is more robust than the smoothness term based on region dissimilarity when the image has complicated background or low contrast. The primal-dual interior point method is applied to optimize the proposed QP in polynomial time. Extensive experiments demonstrate that the proposed method can outperform 10 state-of-the-art methods on three public benchmark datasets