The Applied Research Of The Privacy Protection Technology Of The Data Of National College Entrance Examination And Admission

With its rapid development of computer hardware technology is no longer the main cimitation for the massive data processing and analysis. The needs of society as well as the development of hardware technology greatly promote the prosperity of mining knowledge out of massive data information, which emersely improves data mining technology. With the continuous development of China’s education since the reform and opening up, the original entrance policy, whose defects have been gradually reveaced gradually revealed, can no longer meet the needs of entrance examination, in higher education, leading to the urgent need to reform and improve the college entrance examination system. Education and management departments at all levels, led by the Ministry of Education, are actively engaged in researching and exploring, in order to develop a more scientific and reasonable college entrance examination policy. However, for a long time, the reform and improvement of college entrance examination system is based on the limited statistics and analysis of historical institutional problems that are exposed in the implementation process. This problem-solving problem ignoring the need of current college entrance examination and the requirements of the educational development in the future lacks sound evidence and scientific predictability. Since the implementation of the network admission, education management departments of all levels have accumulated massive admission data of many years, if these massive data are provided to the universities or research management departments for mining and analyzing, a lot of dynamic developing knowledge models can be found and can provide scientific foundation for the education management departments to reform and improve the college entrance examination policies and admission approach, and then, the education management departments can develop the college entrance examination policy that not only satisfies the needs of the present, but also for the future.However, there’s a lot of personal private informationin these data that student do not want to expose, for example, health status, college entrance examination grades, preferred colleages, admission status, bonus score and so on. Based on the consideration of protecting the personal privacy, the education management departments often give up the plan to provide the basis for the college entrance examination system reform and scientific decision through the data mining and analysis of college entrance examination data.Mining the admission information on the basis of ensuring the student’s personal privacy and information security has become the urgent need for education reform and scientific decision-making, and also the premise that education management departments at all levels should obey to mine and analyze college entrance examination data to be mined and analysed.In this paper, based on college entrance examination data in one province from2000to2009that last10years, we analyse the structure of college entrance examination data and the characteristics of various types of privacy protection methods, explore the method that suitable for privacy protection of college entrance examination data, improve the privacy protection of college entrance examination data through the simple association rule mining on a relational database, reduce the amount of computation in privacy protection algorithm, and simplify the data mining of OLAP cubes in college entrance examination data.This paper mainly focuses on the following aspects:1. To analyse privacy protection algorithms in different categories;2. To choose the appropriate algorithms of privacy protection through analysis;3. To sort out the basic theory of K-anonymity privacy protection algorithms;4. To analyse and integrate the college entrance examination data;5. To build a Microsoft SQL Server mining structure and reveal relevant the relationship of the college entrance examination data;6. To optimize the privacy protection method of K-anonymity in college entrance examination data through association rules.