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 behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.

翻译：随着各类在线平台中高调隐私泄露事件的日益频发，用户对其隐私问题愈发关注。推荐系统作为在线平台提供个性化服务的核心组件，其隐私保护问题已引起广泛关注。作为隐私保护的黄金标准，差分隐私已被广泛用于推荐系统的隐私保护中。然而，现有采用差分隐私的推荐系统仅考虑静态且独立的交互行为，因而无法适用于行为具有动态性和依赖性的序列推荐场景。与此同时，这些方法鲜少关注敏感用户特征的隐私风险，大多数仅保护用户反馈信息。在本文中，我们提出了一种新颖的差分隐私序列推荐框架，采用带噪图神经网络方法（记为DIPSGNN），以解决上述局限性。据我们所知，我们是首个在存在依赖交互的序列推荐中实现差分隐私的工作。具体而言，在DIPSGNN中，我们首先利用分段机制保护敏感用户特征。随后，我们基于聚合扰动机制，创新性地向图神经网络的聚合步骤中添加校准噪声。这种带噪图神经网络能够同时保护序列依赖交互并捕捉用户偏好。大量实验表明，与当前最先进的差分隐私推荐系统相比，我们的方法在隐私与准确性之间实现了更优的平衡。