LAUSR

Now various privacy‐preserving techniques have been combined with machine learning to ensure training data security. However, during the training, users were unable to select data and labels adaptively in the server, which made the trained models challenging in meeting user needs. Repeated model training not only wastes server resources, but also reduces query efficiency. In this paper, we combine two privacy‐preserving technologies, searchable encryption and homomorphic encryption, and propose a highly flexible machine training framework. Homomorphic encryption technology is used to encrypt data, and symmetric searchable encryption technology is used to generate blind indexes and trapdoors. The server can perform ciphertext data search through blind query trapdoors and indexes, and can use the searched homomorphic ciphertext for model training. This framework significantly improves the flexibility of training and the fit of the model, and also ensures the security of dynamic data management. The server locally uses the header of the query trap and the storage address of the ciphertext model to build an automatically updatable ciphertext model table. On the basis of ensuring the effectiveness of the model, it greatly improves the efficiency of obtaining the model, effectively prevents redundant training when different users send the same request, and reasonably allocates resources in the server.