On the Utility of Privacy-Preserving Histograms

29 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Smooth sensitivity and sampling in private data analysis (2007, background): “We show how to do this e.ciently for several di.erent functions, including the median and the cost of the minimum spanning tree.”
Toward Privacy in Public Databases (2005, background): “tions [ 6 ], we conjecture it will be possible to obtain cross-training results for mixtures of Gaussians, analogous to our cross-training results for the hypercube described in Section 6 below.”
Our Data, Ourselves: Privacy Via Distributed Noise Generation (2006, unknown): “statistics about a population while preserving the privacy of individuals [1,2, 15,11,14,5, 6 ,4,13].”
Differential Privacy (2006, unknown): “…for the interactive approach have been obtained ( [13, 3, 12] and the present paper), while the non-interactive case has proven to be more diﬃcult, (see [14, 4, 5] ), possibly due to the diﬃculty of supplying utility that has not yet been speciﬁed at the time the sanitization is carried out.”
Calibrating Noise to Sensitivity in Private Data Analysis (2006, background): “We note that k-anonymity [15] and the similarly motivated notion of protection against isolation [7, 8]) have also been in the eye of privacy research.”
Differential privacy for functions and functional data (2012, background): “Histograms are an example of a density estimator that has been “privatized” in previous literature [28, 10].”