Privacy, accuracy, and consistency too: a holistic solution to contingency table release

By Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar. 2007

556 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Management Science and Operations Research, Statistics and Probability, Privacy & Security

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Citations in the corpus

AdaCliP: Adaptive Clipping for Private SGD (2019, methodology): “Diﬀerential privacy deﬁnition was originally used to provide strong privacy guarantees for database querying and since used in several applications [20, 21, 22].”
Marginal Release Under Local Differential Privacy (2017, methodology): “The second ingredient due to Barak et al. [3] is that we can write any marginal β ∈ C as a sum of only a few Hadamard coefﬁcients.”
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): “…[17], Xiao et al. [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…”
On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy (2010, background): “See also [30, 28, 5, 4, 30, 3, 27] for other works dealing with the notion.”
Minimax Optimal Procedures for Locally Private Estimation (2016, methodology): “…that are problematic for other formalisms, have been used to make the case for differential privacy within the computer science literature; see, for example, the articles Evfimievski, Gehrke, and Srikant (2003); Dwork et al. (2006); Barak et al. (2007); Ganta, Kasiviswanathan, and Smith (2008).”
A learning theory approach to non-interactive database privacy (2008, background): “Categories and Subject Descriptors F.2 [Theory of Computation]: Analysis of Algorithms and Problem Complexity General Terms Algorithms, Security, Theory Keywords non-interactive database privacy, learning theory”
Universally utility-maximizing privacy mechanisms (2008, methodology): “This remap is simple, deterministic, and can be computed eﬃciently.”
Composition attacks and auxiliary information in data privacy (2008, background): “Recent work has greatly expanded the applicability of differential privacy and its relaxations, both in the theoretical [15, 6, 14, 4, 28] and applied [17, 3, 25] literature.”
Fingerprinting codes and the price of approximate differential privacy (2013, background): “Then in Section 5.2 we show a stronger lower bound for arbitrary counting queries when α is a varying parameter, thereby proving Theorem 1.2 in the introduction.”
A Simple and Practical Algorithm for Differentially Private Data Release (2010, methodology): “[21] describe an approach to differentially private contingency table release using linear queries defined by the Hadamard matrix.”
EKTELO: A Framework for Defining Differentially-Private Computations (2018, background): “sed extensively to represent high-dimensional workloads and measurements. ϵktelo implicit matrices extend and generalize those matrix constructions. Techniques that use measurements based on wavelets [6] (for range query workloads) and Fourier basis queries [43] (for marginals), are examples in whic”