Development of a Robust Course Evaluation System that Prevents Individual Information Leakage by Employing Input Data Analysis

点击翻译按钮获取中文摘要

Although conventional electronic systems for course evaluation offer various levels of privacy protection, patterns in the input data itself can accidentally lead to leakage of personal information. This type of risk of accidental leakage of personal information also exists in larger scale surveys, as cross tabulation of personal attribution data such as gender, age or major may yield particular cells where only small numbers of respondents are classified. This research demonstrated a new design for survey systems that can automatically prevent this type of unintended information leakage. The basic design consists of a framework which analyzes the input data to find elements that can lead to information leakage and a mechanism to correct such flaws by modifying the information of the questionnaire design in the database.