Abstract
In the era of big data, maximizing the circulation and sharing of data on the basis of ensuring its security is a challenge. Data security classification provides an effective solution for data security protection. This paper proposes a two-stage data classification method based on nine CIA (Confidentiality, Integrity, Availability) indicators, and introduces an expert scoring mechanism to achieve quantitative and precise data classification. In the empirical research part, this paper applies the new method to G Securities Company and first classifies the company’s data into 76 categories. Then the second stage of grading is carried out based on the scores of each index of different categories. The results show that the proposed method is more accurate and robust in terms of data protection and in terms of increasing the value of data sharing and circulation.
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