  Conditional Generative Adversarial Networks (CGANs) exhibit significant
potential in supervised learning model training by virtue of their ability to
generate realistic labeled images. However, numerous studies have indicated the
privacy leakage risk in CGANs models. The solution DPCGAN, incorporating the
differential privacy framework, faces challenges such as heavy reliance on
labeled data for model training and potential disruptions to original gradient
information due to excessive gradient clipping, making it difficult to ensure
model accuracy. To address these challenges, we present a privacy-preserving
training framework called PATE-TripleGAN. This framework incorporates a
classifier to pre-classify unlabeled data, establishing a three-party min-max
game to reduce dependence on labeled data. Furthermore, we present a hybrid
gradient desensitization algorithm based on the Private Aggregation of Teacher
Ensembles (PATE) framework and Differential Private Stochastic Gradient Descent
(DPSGD) method. This algorithm allows the model to retain gradient information
more effectively while ensuring privacy protection, thereby enhancing the
model's utility. Privacy analysis and extensive experiments affirm that the
PATE-TripleGAN model can generate a higher quality labeled image dataset while
ensuring the privacy of the training data.
