  Privacy-preserving computing is crucial for multi-center machine learning in
many applications such as healthcare and finance. In this paper a Multi-center
Privacy Computing framework with Predictions Aggregation (MPCPA) based on
denoising diffusion probabilistic model (DDPM) is proposed, in which
conditional diffusion model training, DDPM data generation, a classifier, and
strategy of prediction aggregation are included. Compared to federated
learning, this framework necessitates fewer communications and leverages
high-quality generated data to support robust privacy computing. Experimental
validation across multiple datasets demonstrates that the proposed framework
outperforms classic federated learning and approaches the performance of
centralized learning with original data. Moreover, our approach demonstrates
robust security, effectively addressing challenges such as image memorization
and membership inference attacks. Our experiments underscore the efficacy of
the proposed framework in the realm of privacy computing, with the code set to
be released soon.
