Secure multiparty learning from the aggregation of locally trained models

In many applications, multiple parties would benefit from a precise learning model trained from the aggregated dataset. However, the trivial method that all the data is aggregated into one datacenter and processed centrally is not appropriate when data privacy is a significant concern. In this paper, we propose a new framework for secure multi-party learning and construct a concrete scheme by incorporating aggregate signature and proxy re-encryption techniques. Unlike the previous solutions for multi-party privacy-preserving machine learning, we don't use encryption algorithm to encrypt the whole dataset or the intermediate values during the training process. In our scheme, secure verifiable computation delegation is utilized to privately label a public dataset from the aggregation of locally trained models. Using these newly generated labeled data items, the participants can update their learning models with great accuracy improvement. Further, we prove that the proposed scheme satisfies the desired security properties, and the experimental analysis on MNIST and HAM10000 shows that it is highly efficient.