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Privacy-preserving cross-domain recommendation (PPCDR) refers to preservingthe privacy of users when transferring the knowledge from source domain totarget domain for better performance, which is vital for the long-termdevelopment of recommender systems. Existing work on cross-domainrecommendation (CDR) reaches advanced and satisfying recommendationperformance, but mostly neglects preserving privacy. To fill this gap, wepropose a privacy-preserving generative cross-domain recommendation (PPGenCDR)framework for PPCDR. PPGenCDR includes two main modules, i.e., stableprivacy-preserving generator module, and robust cross-domain recommendationmodule. Specifically, the former isolates data from different domains with agenerative adversarial network (GAN) based model, which stably estimates thedistribution of private data in the source domain with Renyi differentialprivacy (RDP) technique. Then the latter aims to robustly leverage theperturbed but effective knowledge from the source domain with the raw data intarget domain to improve recommendation performance. Three key modules, i.e.,(1) selective privacy preserver, (2) GAN stabilizer, and (3) robustnessconductor, guarantee the cost-effective trade-off between utility and privacy,the stability of GAN when using RDP, and the robustness of leveragingtransferable knowledge accordingly. The extensive empirical studies on Doubanand Amazon datasets demonstrate that PPGenCDR significantly outperforms thestate-of-the-art recommendation models while preserving privacy.

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation