Big Data Privacy Protection Technology Integrating CNN and Differential Privacy

To solve the difficulty of balancing privacy and availability in big data privacy protection technology, this study integrates the powerful feature extraction ability of convolutional neural network models with the efficiency of differential privacy technology in data privacy protection. An innovative privacy protection method combining gradient adaptive noise and adaptive step size control is proposed. The experiment findings denote that the research method outperforms existing advanced privacy protection technologies in terms of performance, with an average accuracy of 97.68% and a performance improvement of about 20% to 30%. In addition, for larger privacy budgets, increasing the threshold appropriately can further optimize the effectiveness of research methods. This indicates that through refined noise control and step size adjustment, not only can the privacy protection process be optimized, but also the high efficiency and accuracy of data processing can be maintained. In summary, while ensuring data utility, research methods can not only significantly reduce the risk of privacy breaches, but also optimize privacy protection mechanisms, achieving an ideal balance between protecting personal privacy and maximizing data utility. This innovative approach provides an efficient probability distribution function solution for the field of privacy protection, with the potential to promote further development of related technologies and applications.