Differentially Private Histogram Publication for Dynamic Datasets

Li, Haoran; Xiong, Li; Jiang, Xiaoqian; Liu, Jinfei

doi:10.1145/2806416.2806441

Cited by 44 publications

(28 citation statements)

References 32 publications

(48 reference statements)

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“…In addition, Ref. introduced an adaptive sampling approach for dynamic data sets of differentially private histogram publication. However, these methods all attempt to find a reconstruction method to improve the accuracy of the released data, ignoring outliers existing in the original histogram.…”

Section: Related Workmentioning

confidence: 99%

Publishing histograms with outliers under data differential privacy

Han

et al. 2016

Security Comm Networks

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Histograms are important tools for data mining and analysis. Several differentially private publishing schemes for histograms have been proposed recently. Existing differentially private histogram publication schemes have shown that histogram reconstruction is a promising idea for the improvement of publication histograms' accuracy. However, none of these have properly considered the problem outliers in the original histogram, which can cause significant reconstruction errors. Based on the problem, the publication of histogram outliers under differential privacy, this paper puts forward a publication method for histograms with outliers under differential privacy: Outlier‐HistoPub. Our method deals with the count sequence of the original histogram first, using a “global sort” to reduce the degree of alternative distribution (a concept proposed in this paper), which may eliminate the influence of outliers during reconstruction. To avoid individual privacy leakage in the reconstruction process, an exponential mechanism is used to select the most similar adjacent bins of the uniformity distribution histogram to merge each time, and the Laplace mechanism is utilized to generate noisy data to perturb the count sequence of the reconstruction histogram. Experiments prove that the method proposed in this paper can improve the efficiency and accuracy of histogram publication. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Publishing histograms with outliers under data differential privacy

Han

et al. 2016

Security Comm Networks

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“…A histogram publication mechanism Q satisfies -differential privacy ( -DP) [5] , if it outputs a ran-…”

Section: Dp (Differential Privacy)mentioning

confidence: 99%

Different strategies for differentially private histogram publication

Meng

Cui

2017

J. Commun. Inf. Netw.

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Differential privacy is a strong notion for protecting individual privacy in data analysis or publication, with strong privacy guaranteeing security against adversaries with arbitrary background knowledge. A histogram is a representative and popular tool for data publication and visualization tasks. Following the emergence and development of data analysis and increasing release demands, protecting the private data and preventing sensitive information from leakage has become one of the major challenges for histogram publication. In recent years, many approaches have been proposed for publishing histograms with differential privacy. This paper explores the problem of publishing histograms with differential privacy, and provides a systematical summarization of existing research efforts in this field, begining with a discussion of the basic principles and characteristics of the technology. Furthermore, we provide a comprehensive comparison of a series of state-of-the-art histogram publication schemes. Finally, we provide possible suggestions for further expansions of future work in this area.

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“…We observe that U ( n , ε ) can be considered as a general utility form in a series of existing state-of-the-art DP algorithms (e.g. [11, 13, 15, 3, 4, 20, 8, 12, 18, 17]). (i) Count query In the Laplace mechanism, the noisy result of a function f can be represented as f ( D ) + ν , where ν follows

Lap (\frac{{italicΔ}_{f}}{ε})

, and Δ f is the sensitivity related to number of records n .…”

Section: Partitioning Mechanismsmentioning

confidence: 99%

Partitioning-Based Mechanisms Under Personalized Differential Privacy

Xiong

et al. 2017

Advances in Knowledge Discovery and Data Mining

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Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms.

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Differentially Private Histogram Publication for Dynamic Datasets

Cited by 44 publications

References 32 publications

Publishing histograms with outliers under data differential privacy

Publishing histograms with outliers under data differential privacy

Different strategies for differentially private histogram publication

Partitioning-Based Mechanisms Under Personalized Differential Privacy

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