So we here we present our project machine learning models trained on real world data must comply with the privacy loss like the GDPR and the California consumer privacy act, which includes a right to be forgotten. Retreating models from scratch to remove specific user data is computation and costly making machine learning crucial research shows that privacy utility efficiency trade off exists in machine learning, presenting a child. Try-Lama involving accuracy privacy and runtime efficiency current situation solutions for deep neural networks often overlook this balance. A practical machine on any approach should adapt to varying levels of privacy utility and efficiency needed for different tasks the Khan and new frameworks proposed to address this try-lama. There are three modules in our current milk framework, important data selection, progressive Gaussian mechanism, and online in proxy. The data selection module selectively discovers best important data enhancing training speed. The progressive Gaussian mechanism adds Gaussian noise to the prune dataset, allowing the tradeoff between privacy and accuracy. The proxy model is nest trained on the preserved data for a limited number of epochs. But as it runs time and privacy through knowledge transfer, this approach manages to trade off a mod data volume, noise level, and training duration to address the privacy accuracy runtime trillium. In this section, we conduct extensive experiments to validate the effectiveness of common view. Our experiments mainly focus on image classification for 7 for 10 on ResNet 18 under 2 unlearning scenarios. Besides, additional experiments are conducted on C for 100 and the SVHN dataset using VGG 16. In terms of evaluation matrix to AIM2, we evaluate machine learning methods from five perspectives. Here we present the IPR-IM score, which is calculated using geometric mean of all three privacy evaluation matrix to denote a more comprehensive privacy performance of machine learning algorithms. Here we present our first research question here. In this table, we can see that common view has the highest IPR-IM score among all baseline models with an acceptable runtime efficiency relative to other baselines. Yet another research question arise here. To add this question, we systematically adjust the input volume of each module with random forgetting requests on the SVHN dataset, showcasing the ability to control trade-off at various levels. And here, the last research question, we compare the controllability of common view and E-fantuning to you and evaluate the performance of two models based on three crucial factors. As shown in the figure, common view displays superiority, not only in the stability of controlling the trulema, but also a significant margin over overall performance. In conclusion, we identify the trulema between model privacy utility efficiency that existed in machine learning for deep neural networks and future works could focus on extending the work to NLPN Graph domains.