There are tremendous users of social media networks, and they keep growing day by day. In recent years, social media networks have sparked widespread interest among the general public because they offer a simple and appealing form of communication. Users’ data is at risk due to their expanding contact with social networks, necessitating securing their privacy. Users communicate with each other on social media and share information that is vital and private. This user’s information is on the tip of attraction, as many third-party ethical users use it for good causes like increasing the new customers by analyzing their needs. Still, unethical users misuse it for destructive purposes like stealing data, burglary, or personification. And hence the data owner’s task is to preserve the privacy of information. Before publishing such information in the public domain, it needs to be anonymized so that a third party cannot misuse it. Again balancing privacy and utility also need to be focused. The proposed improved Mondrian algorithm anonymizes data by partitioning and enhances privacy and utility balance on the Adult dataset same as with measures PIRL, NCP and DP.

Academic data mining impacts a large number of educational institutions, significantly, playing a prime role in accumulating, studying, and analyzing the academic data. The accumulated academic data can be processed and analyzed for various purposes such as predicting the student academic performance and broadening the retention rate. The prediction of students’ academic performance at the initial stage helps the students to identify their lacking subjects. Accordingly, the predicted results can assist the students to focus more on their deficient subjects so as to improvise their academic performance. Currently, numerous machine learning techniques are being used by the academic institutions to extract and analyze the valuable information ranging from predicting students’ academic performance to the identification of fast and slow learners. This paper intends to propose an ensemble model, using the voting method for preclusive prediction of the student academic performance. The predicted results are being further utilized by the poor performers to concentrate more on their deficit courses. Hence, the instructors can focus on creating and implementing novel strategies or amending the existing pedagogical tools and approaches to aid the slow learners in improvising their performance. The proposed model has been tested on the academic data of an educational institution using the RapidMiner tool.