Differentially private histogram publication

By Jia Xu, Zhenjie Zhang, Xiaokui Xiao, Y. Yang, Ge Yu, Marianne S. Winslett. 2012

344 citations recorded by the source corpus.

How researchers see this paper

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In Artificial Intelligence

Citations in the corpus

Functional Mechanism: Regression Analysis under Differential Privacy (2012, neutral): “…the worst-case error of a given set of count queries; Barak et al. [2] and Ding et al. [8] consider the publication of data cubes; Xu et al. [31] and Li et al. [18] focus on publishing histograms; McSherry and Mironov [20], Rastogi and Nath [24], and McSherry and Mahajan [19] devise methods…”
Differentially Private Hypothesis Transfer Learning (2018, background): “It is a very difficult task to estimate high-dimensional probability densities and releasing the unperturbed distribution or the histogram can be privacy-breaching in itself [42].”
Blowfish privacy: tuning privacy-utility trade-offs using policies (2013, methodology): “Various hierarchical methods have been proposed in the literature [9, 19, 15, 20, 18].”
Locally Differentially Private Data Collection and Analysis (2019, background): “Since the introduction of differential privacy (DP), a large number of DP-based mechanisms [27, 25, 57] have been proposed and applied to numerous scenarios, such as data statistics [54, 61, 11], learning models [1, 40, 59, 38], and systems [29, 6].”
The matrix mechanism: optimizing linear counting queries under differential privacy (2015, neutral): “Practical algorithms are presented to answer linear queries on 1dimensional databases [1,30] and 2-dimensional databases [4,29], respectively.”
Plausible Deniability for Privacy-Preserving Data Synthesis (2017, background): “, histograms [6, 51]) about a dataset, but not full data records.”
Principled Evaluation of Differentially Private Algorithms using DPBench (2015, methodology): “Moreover, a few algorithms [10, 21, 27] assume that the scale of the dataset is known.”
Differentially Private Releasing via Deep Generative Model (2018, methodology): “The existing work can be roughly categorized into supervised settings, such as logistic regression [5] and support vectormachine (SVM) [5, 31], and unsupervised settings, such as publishing histograms [37], releasing contingency tables [38], hypothesis testing [13], collaborative recommendation [42]”
A Data- and Workload-Aware Query Answering Algorithm for Range Queries Under Differential Privacy (2014, methodology): “For data-dependent algorithms, we compare with EFPA [3], P-HP [3], StructureFirst [22],(2) and MWEM [12], all of which are described in Sec.”
Monitoring web browsing behavior with differential privacy (2014, background): “Since then, various works have adopted this deﬁnition for publishing histograms [27], search logs [18], mining data streams [6], and record linkage [3].”
Real-time aggregate monitoring with differential privacy (2012, background): “Most existing works on differentially private data release deal with one-time release of static data [4, 7, 13, 14, 15, 16].”
Differentially Private Data Publishing and Analysis: A Survey (2017, methodology): “Xu [20] provided two partition strategies by minimizing the sum of squared error (SSE) of a set of queries.”
Differentially Private Hierarchical Count-of-Counts Histograms (2018, methodology): “Starting with the simplest application, adding Laplace noise to each count [14], these methods have progressively become more sophisticated and data aware to the point where they take advantage of structure in the data (such as clusters) to improve accuracy (eg., [3, 23, 35, 33]).”
A Stability-based Validation Procedure for Differentially Private Machine Learning (2013, methodology): “We use the algorithm variant due to [19].”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, positive): “Interesting work has been done in both theCDPmodel [8, 22, 30, 33, 34, 39, 54– 56, 58, 70, 71, 75, 85, 89, 90, 100–102, 104, 105, 107] and the LDP model [13, 32, 42, 44, 91, 95–97, 108].”
Differential Privacy in Telco Big Data Platform (2015, background): “…deﬁnition which ensures the outcome of any authenticated query/calculation to be insensitive to any individual record in the DB. DP is preferred due to its strong mathematical boundary of the leaked privacy and has been widely studied in the research community [6, 23, 35, 27, 11, 18, 38, 34].”