  Cross Domain Recommendation (CDR) has been popularly studied to alleviate the
cold-start and data sparsity problem commonly existed in recommender systems.
CDR models can improve the recommendation performance of a target domain by
leveraging the data of other source domains. However, most existing CDR models
assume information can directly 'transfer across the bridge', ignoring the
privacy issues. To solve the privacy concern in CDR, in this paper, we propose
a novel two stage based privacy-preserving CDR framework (PriCDR). In the first
stage, we propose two methods, i.e., Johnson-Lindenstrauss Transform (JLT)
based and Sparse-awareJLT (SJLT) based, to publish the rating matrix of the
source domain using differential privacy. We theoretically analyze the privacy
and utility of our proposed differential privacy based rating publishing
methods. In the second stage, we propose a novel heterogeneous CDR model
(HeteroCDR), which uses deep auto-encoder and deep neural network to model the
published source rating matrix and target rating matrix respectively. To this
end, PriCDR can not only protect the data privacy of the source domain, but
also alleviate the data sparsity of the source domain. We conduct experiments
on two benchmark datasets and the results demonstrate the effectiveness of our
proposed PriCDR and HeteroCDR.
