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Data privacy and ownership are significant in social data science, raisinglegal and ethical concerns. Sharing and analyzing data is difficult whendifferent parties own different parts of it. An approach to this challenge isto apply de-identification or anonymization techniques to the data beforecollecting it for analysis. However, this can reduce data utility and increasethe risk of re-identification. To address these limitations, we present PADME,a distributed analytics tool that federates model implementation and training.PADME uses a federated approach where the model is implemented and deployed byall parties and visits each data location incrementally for training. Thisenables the analysis of data across locations while still allowing the model tobe trained as if all data were in a single location. Training the model on datain its original location preserves data ownership. Furthermore, the results arenot provided until the analysis is completed on all data locations to ensureprivacy and avoid bias in the results.

PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences