Secure MapReduce Framework with Data Compression Model to Evaluate Ideological Evaluation Contribution on Students
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Abstract
Ideological education is the process of transmitting and instilling a particular set of beliefs, values, and worldviews in individuals, with the goal of shaping their attitudes, behaviors, and perceptions. In colleges, ideological education plays an important role in shaping students' values, beliefs, and attitudes toward society, politics, and culture. The primary objective of ideological education in colleges is to promote critical thinking skills, social responsibility, and civic engagement among students. However, the use of big data in ideological education also raises concerns about privacy and ethical considerations. It is important to ensure that students' personal information is kept secure and that data is only used for educational purposes. It is also important to ensure that the use of big data does not lead to bias or discrimination of certain students or groups based on their demographics or backgrounds. This paper constructed a framework of Stream Cipher MapReduce Fractal Index (ScMFI). The ScMFI model performs the processing of information about students in ideological education in colleges. Through the Fractal Index Tree data is compressed with the MapReduce Framework. Finally, the ScMFI model uses the Stream Cipher process for data processing and storing in the cloud environment. The developed ScMFI model contribution of ideological education toward students’ awareness is classified as a machine learning model. The simulation analysis stated that ScMFI model minimizes the data storage, encryption and decryption time.
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