Develop methods for anonymizing data for privacy-preserving AI

This paper looks into the problem of privacy-preserving data publication in cloud computing, which is crucial for preserving the confidentiality of the data and using it for analytical purposes. To address this problem, we have introduced a new heuristic approach to anonymization that enhances data utility while ensuring appropriate levels of data privacy. In our methodology, we apply sophisticated simulation models to evaluate the efficiency of the anonymization method under different data sets. The main results prove that the improvement in the privacy metrics is significant compared to the prior techniques and without making substantial compromises on the usability of the data. Based on these findings, the authors believe that the generalized technique presented in this work could be implemented in genuine cloud computing environments as a powerful tool to address data privacy issues. As the final discussion, this paper presents the real-life significance of implementing this technique and future research recommendations that could expand the scope by experimenting with scalability and more optimization to handle the large volume of data and complications.