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Dataverse 4.0 is here!

Harvard Dataverse has been upgraded to Dataverse 4.0 and is available for all to use at dataverse.harvard.edu

Please note that you will have to enter a new password when logging in for the first time due to stricter password requirements. Passwords in 4.0 must be at least 6 characters and contain at least one number and letter.

Thank you to all the Dataverse users who participated in usability testing and provided feedback. Dataverse 4.0 would not be the same without you!

Dataverse 4.0, Next Week!

At the beginning of next week, April 13th, Harvard Dataverse will be upgraded to Dataverse 4.0! The current version of the Harvard Dataverse (thedata.harvard.edu) will still be available for the next month for you to view. However, you will be able to edit, upload, or download in the new release 4.0 and can benefit from the new functionalities there.  

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Latest Publications

Honaker J. Efficient Use of Differentially Private Binary Trees, in TPDP15: First Workshop on the Theory and Practice of Differential Privacy, London, UK.; 2015.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.
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