Differential privacy has recently become a de facto standard for pri-vate statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. How-ever, most of them focus on “one-time " release of a static dataset and do not adequately address the increasing need of releasing se-ries of dynamic datasets in real time. A straightforward applica-tion of existing histogram methods on each snapshot of such dy-namic datasets will incur high accumulated error due to the com-posibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the prob-lem of releasing series of dynamic datasets in real time with dif-ferential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the cur-rent snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic thresh-old adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods. Categories and Subject Descriptors H.2.7 [Database Administration]: [Security, integrity, and protec-