Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach. (arXiv:2309.11515v2 [cs.CR] UPDATED)

With increasing frequency of high-profile privacy breaches in various online
platforms, users are becoming more concerned about their privacy. And
recommender system is the core component of online platforms for providing
personalized service, consequently, its privacy preservation has attracted
great attention. As the gold standard of privacy protection, differential
privacy has been widely adopted to preserve privacy in recommender systems.
However, existing differentially private recommender systems only consider
static and independent interactions, so they cannot apply to sequential
recommendation where …

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