Replicable Privacy: Enabling Replication on Sensitive Internet Data

Suso Baleato Harvard University & University of Santiago de Compostela
James Honaker Harvard University
Merce Crosas Harvard University


In this paper we present the first results of a privacy-preserving system designed to enable safe sharing and replication of statistical analysis computed from sensitive datasets. Our system is composed of three elements, all of them made available to the scientific community thanks to an effort lead by the Institute for Quantitative Social Science at Harvard University. First, we use differential privacy, a privacy-preserving technique that avoids re-identification while preserving the statistical properties of the sensitive dataset. Second, we use the Dataverse open source software to share the resulting statistics consistently with FAIR principles, including automatic citation, persistent identifiers and data provenance. Third, we apply a simplified Datatags implementation to enable access to any sensitive dataset required for replication.


Thumbnail image of replicable-privacy.pdf


Log in or register with APSA to comment open_in_new
Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy open_in_new – please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here open_in_new .
This site is protected by reCAPTCHA and the Google Privacy Policy open_in_new and Terms of Service open_in_new apply.