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Protecting locations with differential privacy under temporal correlations (2015)

by Y Xiao, L Xiong
Venue:In CCS
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Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach

by Haoran Li, Li Xiong, Xiaoqian Jiang, Jinfei Liu
"... 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 a ..."
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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-
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...share a series of dynamic private datasets over individual users while guaranteeing their privacy. The current state-of-the-art standard for privacy preserving data publishing is differential privacy =-=[9, 27]-=-, which requires that the output released by a data provider be perturbed by a randomized algorithm A, so that the output of A remains roughly the same even if any individual tuple in the input data i...

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