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An Axiomatic View of Statistical Privacy and Utility

Daniel Kifer, Bing-Rong Lin

Differential Privacy in Practice: Expose your Epsilons!

Cynthia Dwork, Nitin Kohli, Deirdre Mulligan

Differentially Private Confidence Intervals for Empirical Risk Minimization

Yue Wang, Daniel Kifer, Jaewoo Lee

Gradual Release of Sensitive Data under Differential Privacy

Fragkiskos Koufogiannis, Shuo Han, George J. Pappas

Towards a Systematic Analysis of Privacy Definitions

Bing-Rong Lin, Dan Kifer

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, Benjamin Livshits

Per-instance Differential Privacy

Yu-Xiang Wang

Calibrating Noise to Sensitivity in Private Data Analysis

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith

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Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel

Differential Privacy on Finite Computers

Victor Balcer, Salil Vadhan

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft

A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples

Raj Chetty, John N Friedman

Differential Privacy for Statistics: What we Know and What we Want to Learn

Cynthia Dwork, Adam Smith

Make Up Your Mind: The Price of Online Queries in Differential Privacy

Mark Bun, Thomas Steinke, Jonathan Ullman

On the 'Semantics' of Differential Privacy: A Bayesian Formulation

Shiva P. Kasiviswanathan, Adam Smith

Between Pure and Approximate Differential Privacy

Thomas Steinke, Jonathan Ullman

Random Differential Privacy

Robert Hall, Larry Wasserman, Alessandro Rinaldo

Differential Privacy for Protecting Multi-dimensional Contingency Table Data: Extensions and Applications

Xiaolin Yang, Stephen E. Fienberg, Alessandro Rinaldo

Featherweight PINQ

Hamid Ebadi, David Sands

On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy

Cynthia Dwork, Moni Naor

Statistical Approximating Distributions Under Differential Privacy

Yue Wang, Daniel Kifer, Jaewoo Lee, Vishesh Karwa

A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms

Bai Li, Vishesh Karwa, Aleksandra Slavković, Rebecca Carter Steorts

Statistical Disclosure Limitation: New Directions and Challenges

Natalie Shlomo

Minimaxity, Statistical Thinking and Differential Privacy

Larry Wasserman

Dual Query: Practical Private Query Release for High Dimensional Data

Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu
1 - 25 of 45 items 1 2 > >> 

about2

The Journal of Privacy and Confidentiality is an open-access multi-disciplinary journal whose purpose is to facilitate the coalescence of research methodologies and activities in the areas of privacy, confidentiality, and disclosure limitation. The JPC seeks to publish a wide range of research and review papers, not only from academia, but also from government (especially official statistical agencies) and industry, and to serve as a forum for exchange of views, discussion, and news. For more information, see the About the Journal page.

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