Privacy-Preserving Datamining on Vertically Partitioned Databases

By Cynthia Dwork, Kobbi Nissim. 2004

428 citations recorded by the source corpus.

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

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Citations in the corpus

Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “Vaidya and Clifton (2002); Evﬁmievski et al. (2004); Dwork and Nissim (2004) are among the early studies that investigate the privacy issue of querying vertically partitioned data.”
Marginal Release Under Local Differential Privacy (2017, background): “Differential Privacy (DP) [8, 11, 10], unlike its precursor privacy deﬁnitions, provides semantic mathematical promises on individuals’ privacy, interpreted as statistical properties of the output distribution of a randomized algorithm.”
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “Vertically-partitioned databases have been also studied from the perspective of privacy-preserving querying of statistical databases [22].”
Tight Lower Bounds for Differentially Private Selection (2017, methodology): “We can ensure differential privacy by adding carefully calibrated noise to the empirical mean X to obtain a noisy empirical mean X̃ [13], [14], [15], [12].”
Local, Private, Efficient Protocols for Succinct Histograms (2015, background): “Local differentially private protocols for frequency estimation are used in the Chrome web browser (Erlingsson et al. [9], Fanti et al. [11]), and can be used as the basis of other estimation tasks (see Mishra and Sandler [21], Dwork and Nissim [5]).”
Boosting and Differential Privacy (2010, methodology): “Moreover, we revisit the “evolution of confidence” arguments due to Dinur, Dwork, and Nissim [5], [12] and show that, for achieving (ε, δ) differential privacy of k-fold composition, the privacy parameter ε degrades as kε(2) + √ kε rather than linearly as kε.”
On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy (2010, neutral): “Although the effort seems to have been abandoned for over two decades, the work of Denning [10] is closest in spirit to the line of research recently pursued in [17, 4, 16].”
Learning privately from multiparty data (2016, background): “It provides a strict upper bound on the privacy loss against any adversary (Dwork & Nissim, 2004; Dwork et al., 2006; Dwork, 2006).”
Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds (2014, background): “Differential privacy is a rigorous notion of privacy that emerged from a line of work in theoretical computer science and cryptography [19, 6, 21].”
Distributed Private Data Analysis: On Simultaneously Solving How and What (2008, methodology): “A criterion for functions that preserve privacy of individual entries, differential privacy, has evolved in a sequence of recent works [7, 15, 14, 2, 11, 8, 9].”
A learning theory approach to non-interactive database privacy (2008, neutral): “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”
Preserving Statistical Validity in Adaptive Data Analysis (2014, background): “Differential privacy emerged from a line of work [13, 18, 10], culminating in the definition given by [16].”
Universally utility-maximizing privacy mechanisms (2008, background): “Intuitively, the probability of every response of the privacy mechanism — and hence the probability of a successful privacy attack following an interaction with the mechanism — is, up to a controllable α factor, independent of whether a given user “opts in” or “opts out” of the database.”
Composition attacks and auxiliary information in data privacy (2008, background): “This condition was relaxed somewhat in other papers [10, 15, 6, 13, 8, 29, 25].”
Fingerprinting codes and the price of approximate differential privacy (2013, background): “Some of the earliest work in differential privacy [12, 18, 5, 14] gave an algorithm—the so-called Laplace mechanism— whose sample complexity is Θ̃(|Q|1/2), and thus incurs a large price of differential privacy.”
A Simple and Practical Algorithm for Differentially Private Data Release (2010, background): “Against this backdrop, diﬀerential privacy [4, 11, 5] has emerged as a compelling privacy deﬁnition that allows one to understand this trade-oﬀ via formal, provable guarantees.”