Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy

Aleksandra B. Slavkovic; Adam Smith

doi:10.29012/jpc.v4i1.609

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Published: Jul 20, 2012

DOI: https://doi.org/10.29012/jpc.v4i1.609

Keywords:

machine learning, statistics, cryptography, data mining

Aleksandra B. Slavkovic

Department of Statistics, Penn State University, University Park, PA

Adam Smith

Department of Computer Science and Engineering, Penn State University, University Park, PA

Abstract

This special issue presents papers based on talks from a workshop on "Statistical and Learning-Theoretic Challenges in Data Privacy" held at UCLA's Institute for Pure and Applied Mathematics (IPAM), February 22–26, 2010.

How to Cite

Slavkovic, Aleksandra B., and Adam Smith. 2012. “Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy”. Journal of Privacy and Confidentiality 4 (1). https://doi.org/10.29012/jpc.v4i1.609.

Issue

Vol. 4 No. 1 (2012)

Section

Editorial

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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.