Machine learning using differential privacy

In this section, our objective is to develop a machine learning classification model that can accurately distinguish between fraudulent and genuine credit card transactions. To ensure privacy protection, we will also apply differential privacy techniques to the model. The classification model will be trained on a labeled dataset consisting of historical credit card transactions, where each transaction is labeled as either fraudulent or genuine. Popular machine learning algorithms such as logistic regression, decision trees, or neural networks can be applied to build the classification model and will make use of neural networks in our case.

To incorporate differential privacy, we will leverage techniques such as the addition of noise to the training process and the use of privacy-preserving algorithms. These techniques ensure that the model’s training process and subsequent predictions do not compromise the privacy of individual...