Vol. 7 No. 3 (2016) | Journal of Privacy and Confidentiality

This issue was first published at Research Showcase @ CMU (http://repository.cmu.edu/jpc/vol7/iss3/). Research Showcase @ CMU is Carnegie Mellon University’s institutional repository.

Editor: Stephen E. Fienberg
Managing Editor: Kira Bokalders

DOI: https://doi.org/10.29012/jpc.v7i3

Published: 2017-05-30

How Will Statistical Agencies Operate When All Data Are Private?

John M Abowd

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Calibrating Noise to Sensitivity in Private Data Analysis

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith

17-51

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On the Meaning and Limits of Empirical Differential Privacy

Anne-Sophie Charest, Yiwei Hou

53-66

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Practical Data Synthesis for Large Samples

Gillian M Raab, Beata Nowok, Chris Dibben

67-97

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A New Data Collection Technique for Preserving Privacy

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

99-129

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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.