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.

随着各种在线平台上备受瞩目的隐私泄露事件的发生频率不断增加，用户越来越关注自己的隐私。而推荐系统是网络平台提供个性化服务的核心组成部分，因此其隐私保护备受关注。作为隐私保护的黄金标准，差分隐私已被广泛采用来保护推荐系统中的隐私。然而，现有的差分隐私推荐系统仅考虑静态和独立的交互，因此它们不能应用于行为动态和依赖的顺序推荐。同时，对于敏感用户特征的隐私风险却很少关注，大多只保护用户反馈。在这项工作中，我们提出了一种新颖的差异私有序列推荐框架，采用噪声图神经网络方法（表示为 DIPSGNN）来解决这些限制。据我们所知，我们是第一个在具有依赖交互的顺序推荐中实现差异隐私的人。具体来说，在 DIPSGNN 中，我们首先利用分段机制来保护敏感的用户特征。然后，我们基于聚合扰动机制，创新性地将校准噪声添加到图神经网络的聚合步骤中。而且，这种噪声图神经网络可以保护顺序相关的交互并同时捕获用户偏好。大量的实验证明了我们的方法在隐私和准确性之间更好的平衡方面优于最先进的差分隐私推荐系统。