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A New Data Collection Technique for Preserving Privacy

Samuel S Wu, Shigang Chen, Deborah L Burr, Long Zhang

99-129

An Axiomatic View of Statistical Privacy and Utility

Daniel Kifer, Bing-Rong Lin

Per-instance Differential Privacy

Yu-Xiang Wang

Heterogeneous Differential Privacy

Mohammad Alaggan, Sébastien Gambs, Anne-Marie Kermarrec

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

Calibrating Noise to Sensitivity in Private Data Analysis

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith

17-51

Statistical Disclosure Limitation: New Directions and Challenges

Natalie Shlomo

An Evaluation Framework for Privacy-Preserving Record Linkage

Dinusha Vatsalan, Peter Christen, Christine M. O'Keefe, Vassilios S. Verykios

Privacy via the Johnson-Lindenstrauss Transform

Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, Nina Mishra

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

Raj Chetty, John N Friedman

A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms

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

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

Cynthia Dwork, Adam Smith

Differential Privacy on Finite Computers

Victor Balcer, Salil Vadhan

Local Differential Privacy for Evolving Data

Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

Privacy-Preserving Data Sharing in High Dimensional Regression and Classification Settings

Stephen E. Fienberg, Jiashun Jin

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

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

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

Mark Bun, Thomas Steinke, Jonathan Ullman

Differentially Private Ordinary Least Squares

Or Sheffet

Dual Query: Practical Private Query Release for High Dimensional Data

Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

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

Xiaolin Yang, Stephen E. Fienberg, Alessandro Rinaldo

Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation

John M. Abowd, Matthew J. Schneider, Lars Vilhuber

Privacy-Preserving Data Sharing for Genome-Wide Association Studies

Caroline Uhler, Aleksandra B. Slavkovic, Stephen E. Fienberg

Featherweight PINQ

Hamid Ebadi, David Sands

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

Shiva P. Kasiviswanathan, Adam Smith
1 - 25 of 41 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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