In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Recent deep neural network (DNN)-based recommender system research have made significant progress in improving prediction accuracy, which is largely attributed to the access to a large amount of users' personal data collected from users' devices and then centrally stored in the cloud server. However, as there are rising concerns around the globe on user privacy leakage in the online platform, the public is becoming anxious by such abuse of user privacy. Therefore, it is urgent and beneficial to develop a recommender system that can achieve both high prediction accuracy and high degree of user privacy protection. To this end, we propose a DNN-based recommendation model called PrivRec running on the decentralized federated learning (FL) environment, which ensures that a user's data never leaves his/her during the course of model training. On the other hand, to better embrace the data heterogeneity commonly existing in FL, we innovatively introduce a first-order meta-learning method that enables fast in-device personalization with only few data points. Furthermore, to defense from potential malicious participant that poses serious security threat to other users, we develop a user-level differentially private DP-PrivRec model so that it is unable to determine whether a particular user is present or not solely based on the trained model. Finally, we conduct extensive experiments on two large-scale datasets in a simulated FL environment, and the results validate the superiority of our proposed PrivRec and DP-PrivRec.
翻译:在移动互联网时代,推荐人系统已成为帮助用户发现有用项目并从而缓解信息超载问题的不可替代的工具。最近深层神经网络(DNN)基于推荐人系统的研究在提高预测准确性方面取得了显著进展,这主要归功于对从用户设备收集的大量用户个人数据的访问,然后集中储存在云层服务器中。然而,由于全球范围内对用户隐私在在线平台中渗漏的关切日益提高,公众对滥用用户隐私问题日益感到焦虑。因此,开发一个既能达到高预测准确性和高程度用户保密保护的推荐人系统是紧迫和有益的。为此,我们提出了一个名为PriivRec的基于DNN的建议模型模型模型模型模型,在分散化的联邦化学习环境中运行,这在很大程度上是由于用户个人数据在分散化学习(FL)环境中收集了大量个人数据,这确保用户在模型培训过程中从未离开自己的个人数据。另一方面,为了更好地接受FL通常存在的数据模型的高度模型,我们创新地引入了第一个序列元学习方法,使个人快速消化,只有很少几个数据级的用户隐私保密性保护。此外,我们从恶意的用户安全级别确定另一个潜在的潜在威胁。