A differential privacy-preserving deep learning caching framework for heterogeneous communication network systems

Caching and artificial intelligence technology are means to improve the content transmission rate and solve the delay problem in heterogeneous communication networks, which provide an important technical basis for telemedicine diagnosis, disease monitoring, and other medical and healthcare fields. However, since the data used for caching recommendations and mining usually includes a lot of sensitive information, collecting these data will inevitably cause users to worry about personal privacy security. This paper proposes a differential privacy-preserving deep learning caching framework (DP-DLCF) for heterogeneous communication networks. First, the multivariate randomized response mechanism is used to perturb the mobile client's sensitive information while introducing the least square method to balance the data privacy and utility, and realize the adaptive allocation of the privacy budget. Then use selective integrated learning methods to further improve the local cache accuracy of the neural network training model. Therefore, the framework not only improves the prediction accuracy of the cache network but also solves the problem of privacy leakage in data sharing. Finally, four simulation examples verify the DP-DLCF framework's effectiveness.