Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving
the privacy of users when transferring the knowledge from source domain to
target domain for better performance, which is vital for the long-term
development of recommender systems. Existing work on cross-domain
recommendation (CDR) reaches advanced and satisfying recommendation
performance, but mostly neglects preserving privacy. To fill this gap, we
propose a privacy-preserving generative cross-domain recommendation (PPGenCDR)
framework for PPCDR. PPGenCDR includes two main modules, i.e., stable
privacy-preserving generator module, and robust cross-domain recommendation
module. Specifically, the former isolates data from different domains with a
generative adversarial network (GAN) based model, which stably estimates the
distribution of private data in the source domain with Renyi differential
privacy (RDP) technique. Then the latter aims to robustly leverage the
perturbed but effective knowledge from the source domain with the raw data in
target domain to improve recommendation performance. Three key modules, i.e.,
(1) selective privacy preserver, (2) GAN stabilizer, and (3) robustness
conductor, guarantee the cost-effective trade-off between utility and privacy,
the stability of GAN when using RDP, and the robustness of leveraging
transferable knowledge accordingly. The extensive empirical studies on Douban
and Amazon datasets demonstrate that PPGenCDR significantly outperforms the
state-of-the-art recommendation models while preserving privacy.

本研究提出了一种基于生成对抗网络和 Renyi 差分隐私技术的隐私保护跨域推荐框架，在保证用户隐私的同时，显著提高了模型的推荐性能。