Differentially private data cubes: optimizing noise sources and consistency

By Bolin Ding, Marianne S. Winslett, Jiawei Han, Zhenhui Jessie Li. 2011

163 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Marginal Release Under Local Differential Privacy (2017, background): “Ding et al. [7] suggest computing low dimensional marginals by selecting a subset of high dimensional marginals that add the least amount of noise by posing subset selection as a constrained optimization problem and propose a greedy approximation.”
Signal Processing and Machine Learning with Differential Privacy (2013, background): “…will not discuss some of the progress made in software systems engineering for differential privacy [25]–[27], algorithms for computing histo grams and contingency tables [28], [29], or the large body of work on privacypreserving data release (references can be found in recent works [18], [30]).”
Functional Mechanism: Regression Analysis under Differential Privacy (2012, background): “…[30], and Cormode et al. [6] present methods for minimizing 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…”
A Simple and Practical Algorithm for Differentially Private Data Release (2010, methodology): “We consider datacube release as studied by [3] and ﬁnd that our general-purpose algorithm improves on specialized algorithms designed to optimize various criteria.”
Differential privacy based on importance weighting (2013, background): “…dataset into several groups according to similarity or entropy (this step either involves randomness in order not to destroy privacy, or is data-independent), add noise to the counts of samples in each group, and publish the noisy counts (Xiao et al. 2010; Mohammed et al. 2011; Ding et al. 2011).”
Regression Model Fitting under Differential Privacy and Model Inversion Attack (2015, background): “…[ Chaudhuri and Monteleoni, 2008; Zhang et al. , 2012 ] , publishing contingency tables [Xiao et al. , 2010; Barak et al. , 2007] or data cubes [Ding et al. , 2011], privacy preserving integrated queries [McSherry, 2009], synthetic graph , 2013; Mir and Wright, 2009; Sala et al. , 2011 ] ,…”
Differentially Private Hierarchical Count-of-Counts Histograms (2018, background): “Ding et al. [13] provided extensions to lattices in order to answer data cube queries instead of just range queries.”
The matrix mechanism: optimizing linear counting queries under differential privacy (2015, methodology): “[6] considered a special collection of marginal queries, called data cubes, which are answered by a subset of all data cube queries selected via a greedy algorithm.”
Semantic Security: Privacy Definitions Revisited (2013, background): “The principle of differential privacy was developed in a series of works [6] [9] [2] [22] [19] [5] [3] [41] [16].”
A Data- and Workload-Aware Query Answering Algorithm for Range Queries Under Differential Privacy (2014, background): “Other strategies have been developed for marginal queries [5, 7].”
Differentially Private Hierarchical Group Size Estimation (2018, background): “[13] provided extensions to lattices in order to answer data cube queries instead of just range queries.”