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Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy Framework

Department of Commerce Internet Policy Task Force

Protecting Consumer Privacy in an Era of Rapid Change–A Proposed Framework for Businesses and Policymakers

FTC Staff

Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy

A. Anonymous

Statistical Déjà Vu: The National Data Center Proposal of 1965 and Its Descendants

Rebecca Kraus

An Axiomatic View of Statistical Privacy and Utility

Daniel Kifer, Bing-Rong Lin

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

Per-instance Differential Privacy

Yu-Xiang Wang

Differentially Private Confidence Intervals for Empirical Risk Minimization

Yue Wang, Daniel Kifer, Jaewoo Lee

Heterogeneous Differential Privacy

Mohammad Alaggan, Sébastien Gambs, Anne-Marie Kermarrec

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

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

Improving User Choice Through Better Mobile Apps Transparency and Permissions Analysis

Ilaria Liccardi, Joseph Pato, Daniel J. Weitzner

Silent Listeners: The Evolution of Privacy and Disclosure on Facebook

Fred Stutzman, Ralph Gross, Alessandro Acquisti

Featherweight PINQ

Hamid Ebadi, David Sands

Calibrating Noise to Sensitivity in Private Data Analysis

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith

17-51

A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms

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

The Science and Technology of Privacy Protection: Appendix L of "Protecting Individual Privacy in the Struggle Against Terrorists"

William J. Perry, Charles M. Vest

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

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

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

Cynthia Dwork, Adam Smith

Dual Query: Practical Private Query Release for High Dimensional Data

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

On Privacy and Public Data: a Study of data.gov.uk

Andrew C. Simpson

Top-Coding and Public Use Microdata Samples from the U.S. Census Bureau

Nicole Crimi, William Eddy

Commentary: Future U.S. National Statistics Use of Administrative Data

George Duncan

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

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

Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation

John M. Abowd, Matthew J. Schneider, Lars Vilhuber
1 - 25 of 39 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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