Privacy Preservation in Federated Learning, its Attacks and Defenses using SMC-GAN

When using collaborative machine learning, maintaining privacy is a major concern because different parties want to train a model on their own data without sharing it with others. In collaborative machine learning, the approaches of Secure Multiparty Computation (SMC) and Generative Adversarial Networks (GAN) can be utilized to protect privacy. While GANs can create artificial data samples that are comparable to the real data, SMC allows participants to collaboratively compute a function without disclosing their inputs. In collaborative machine learning, this work provides a method for privacy preservation utilizing SMC and GANs. The proposed approach can produce high model accuracy while maintaining privacy, according to experimental data. The method is assessed using a number of benchmark datasets and contrasted with other privacy-preserving strategies. The findings demonstrate that, in terms of model correctness and privacy preservation, the suggested approach performs better than competing techniques.