With device-to-device (D2D) communication, user equipment can share data with each other without the involvement of network infrastructures. In order to maintain the Quality of Service (QoS) and Quality of Experience (QoE) for user applications in D2D communications, most existing schemes use proactive content caching that needs to predict content popularity before making caching decisions which may result in privacy leakage, since the information of users is collected to train a deep learning-based model to predict content popularity. Therefore, it is crucial to guarantee secure data collection in machine learning-based framework. In this paper, we propose a privacy-preserving D2D caching scheme with a passive content caching strategy based on node importance, which can deliver more efficient caching and prevent the potential leakage of user privacy. The scheme is based on softwaredefined networking (SDN), in which the controller is responsible for calculating node importance of devices according to the information of requests and encounters collected by SDN switches. Base station will decide which device can establish reliable and secure communication with content requester based on historical information. The simulation results show that the proposed strategy can outperform other D2D caching strategies in terms of cache hit rate and data rate.

中文翻译：

---

设备到设备通信的隐私保护缓存方案

通过设备到设备（D2D）通信，用户设备可以彼此共享数据，而无需网络基础架构的参与。为了维护D2D通信中用户应用程序的服务质量（QoS）和体验质量（QoE），大多数现有方案使用主动内容缓存，该内容缓存需要在做出可能导致隐私泄漏的缓存决策之前预测内容流行度，因为收集用户信息以训练基于深度学习的模型来预测内容受欢迎程度。因此，在基于机器学习的框架中确保安全数据收集至关重要。在本文中，我们提出了一种基于节点重要性的具有被动内容缓存策略的隐私保护D2D缓存方案，这样可以提供更有效的缓存，并防止潜在的用户隐私泄露。该方案基于软件定义网络（SDN），其中控制器负责根据SDN交换机收集的请求和遇到的信息来计算设备的节点重要性。基站将根据历史信息决定哪个设备可以与内容请求者建立可靠和安全的通信。仿真结果表明，所提出的策略在缓存命中率和数据速率方面可以胜过其他D2D缓存策略。基站将根据历史信息决定哪个设备可以与内容请求者建立可靠而安全的通信。仿真结果表明，所提出的策略在缓存命中率和数据速率方面可以胜过其他D2D缓存策略。基站将根据历史信息决定哪个设备可以与内容请求者建立可靠和安全的通信。仿真结果表明，所提出的策略在缓存命中率和数据速率方面可以胜过其他D2D缓存策略。