The use of TensorFlow Privacy, which provides differential privacy mechanisms for machine learning models, introduces additional computational overhead compared to standard TensorFlow model training. This increase in computational time is a direct result of the extra mathematical operations required to achieve differential privacy guarantees during the training process. Differential Privacy (DP) is a rigorous mathematical