  This paper demonstrates the potential of statistical disclosure control for
protecting the data used to train recommender systems. Specifically, we use a
synthetic data generation approach to hide specific information in the
user-item matrix. We apply a transformation to the original data that changes
some values, but leaves others the same. The result is a partially synthetic
data set that can be used for recommendation but contains less specific
information about individual user preferences. Synthetic data has the potential
to be useful for companies, who are interested in releasing data to allow
outside parties to develop new recommender algorithms, i.e., in the case of a
recommender system challenge, and also reducing the risks associated with data
misappropriation. Our experiments run a set of recommender system algorithms on
our partially synthetic data sets as well as on the original data. The results
show that the relative performance of the algorithms on the partially synthetic
data reflects the relative performance on the original data. Further analysis
demonstrates that properties of the original data are preserved under
synthesis, but that for certain examples of attributes accessible in the
original data are hidden in the synthesized data.
