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Honaker J. Efficient Use of Differentially Private Binary Trees. Submitted.Abstract
Binary trees can be made differentially private by adding noise to every node and leaf.  In such form they allow multifaceted exploration of a variable without revealing any individual information.  While a differentially private binary tree can be used and read just like its conventional exact-valued analog, realizing that different combinations of nodes contain overlapping answers to the same information allows us to bring the statistical properties of multiple measurements under measurement error to noisy binary trees to create statistically efficient node estimates.  We construct estimators that correctly use all available information in the tree, thus decreasing the error of nodes by up to eighty percent for the same level of privacy protection.
Castro E, Garnett A.

Building a Bridge Between Journal Articles and Research Data: The PKP-Dataverse Integration Project

. International Journal of Digital Curation [Internet]. 2014;9(1):176-184. Publisher's VersionAbstract
A growing number of funding agencies and international scholarly organizations are requesting that research data be made more openly available to help validate and advance scientific research. Thus, this is an opportune moment for research data repositories to partner with journal editors and publishers in order to simplify and improve data curation and publishing practices. One practical example of this type of cooperation is currently being facilitated by a two year (2012-2014) one million dollar Sloan Foundation grant, integrating two well-established open source systems: the Public Knowledge Project’s (PKP) Open Journal Systems (OJS), developed by Stanford University and Simon Fraser University; and Harvard University’s Dataverse Network web application, developed by the Institute for Quantitative Social Science (IQSS). To help make this interoperability possible, an OJS Dataverse plugin and Data Deposit API are being developed, which together will allow authors to submit their articles and datasets through an existing journal management interface, while the underlying data are seamlessly deposited into a research data repository, such as the Harvard Dataverse. This practice paper will provide an overview of the project, and a brief exploration of some of the specific challenges to and advantages of this integration.
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