Privacy-Preserving News Recommendation Model Learning (2020)

Abstract: News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices. Our method can leverage the useful information in the behaviors of massive number users to train accurate news recommendation models and meanwhile remove the need of centralized storage of them. More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device. The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server. Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection. The updated global model is then distributed to each user device for local model update. We repeat this process for multiple rounds. Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.

## introduction with the development of internet and mobile internet, online news websites and apps such as yahoo! news foot_0 and toutiao foot_1 have become very popular for people to obtain news information @xcite . since massive news articles are posted online every day, users of online news services face heavy information overload @xcite . different users usually prefer different news information. thus, personalized news recommendation, which aims to display news articles to users based on their personal interest, is a useful technique to improve user experience and has been widely used in many online news services @xcite . the research of news recommendation has attracted many attentions from both academic and industrial fields @xcite @xcite @xcite . many news recommendation methods have been proposed in recent years @xcite @xcite . these methods usually recommend news based on the matching between the news representation learned from news content and the user interest representation learned from historical user behaviors on news. for example, @xcite proposed to learn news representations from the content of news articles via autoencoder, and learn user interest representations from the clicked news articles via gated recurrent unit (gru) network. they ranked the candidate news articles using the direct dot product of the news and user interest representations. these approaches all rely on the centralized storage of user behavior data such as news click histories for model training. however, users' behaviors on news websites and apps are privacy-sensitive, the leakage of which may bring catastrophic consequences. unfortunately, the centralized storage of user behavior data in server may lead to high privacy concerns from users and risks of large-scale private data leakage. in this paper, we propose a privacy-preserving method for news recommendation model training. instead of storing user behavior data on a central server, in our method it is locally stored on (and never leaves) users' personal devices, which can effectively reduce the privacy concerns and risks @xcite . since the behavior data of a single user is insufficient for model training, we propose a federated learning based framework named fednewsrec to coordinate massive user devices to collaboratively learn an accurate news recommendation model without the need to centralized storage of user behavior data. in our framework, on each user device we keep a local copy of the news recommendation model. since the user behaviors on news websites or apps stored in user device can provide important supervision information of how the current model performs, we compute the model gradients based on these local behaviors. the local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global news recommendation model maintained in the server. the updated global model is then distributed to each user device for local model update. we repeat this process for multiple rounds until the training converges. since the local model gradients may also contain some implicit private information of users' behaviors on their devices, we apply the local differential privacy (ldp) technique to these local model gradients before uploading them to server, which can better protect user privacy at the cost of slight performance sacrifice. we conduct extensive experiments on a real-world dataset. the results show that our method can achieve satisfactory performance in news recommendation by coordinating massive users for model training, and at the same time can well protect user privacy. the major contributions of this work include: ## fednewsrec for privacy-preserving news recommendation in this section we introduce our fednewsrec method for privacy-preserving news recommendation model training. we first describe the news recommendation model. then we describe the details of fednewsrec. ## conclusion in this paper, we propose a privacy-preserving method for news recommendation model training. different from existing methods which rely on centralized storage of user behavior data, in our method the user behaviors are locally stored on user devices. we propose a fednewsrec framework to coordinate a large number of users to collectively train accurate news recommendation models from the behavior data of these users without the need to upload it. in our method each user client computes local model gradients based on the user behaviors on device, and sends them to server. the server coordinates the training process and maintains a global news recommendation model. it aggregates the local model gradients from massive users and updates the global model using the aggregated gradient. then the server sends the updated model to user clients and this process is repeated for multiple rounds. in order to further protect the private information in the local model gradients, we apply local differential privacy to them by adding laplace noise. the experiments on real-world dataset show that our method can achieve comparable performance with sota news recommendation methods, and meanwhile can better protect user privacy.