Database attribute sensitivity quantification method based on attack probability

Abstract

The invention discloses an attack probability-based database attribute sensitivity quantification method, which comprises the following steps of: 1) endowing each column in a database with the probability of possibly obtaining the column in advance by an attacker; 2) inputting the database into a database primary key analysis system to obtain all primary keys and composite primary keys of the database; 3) sorting each column in the database according to a result output in the step 2), and finding out which primary keys and composite primary keys each column appears in; 4) calculating the probability that each column in the database is successfully attacked; and 5) performing sensitivity quantification and sorting on each column in the database according to the probability calculated in thestep 4 that each column in the database is successfully attacked, outputting sensitivity quantification and sorting results, and completing database attribute sensitivity quantification based on attack probability. According to the method, sensitivity quantification and sorting can be carried out on all attributes in the database according to the attack success probability of an attacker, and theaccuracy is high.

Description

technical field [0001] The invention belongs to the technical field of data security, and relates to a database attribute sensitivity quantification method based on attack probability. Background technique [0002] With the advent of the era of big data, various types of data play an increasingly important role in people's daily life. While all kinds of data services have brought countless conveniences to people, they have also created a series of security problems. For example, personal information such as mobile phone numbers, photos, work units, and birth dates in social networks, as well as real-time information such as user operation logs and location information, will be stored in the database by the service provider. Once these data are attacked, In the event of data leakage, all private information of users may flow into the hands of criminals, and the consequences will be disastrous. Therefore, the management risks brought about by social security big data are bec...

AI Technical Summary

Problems solved by technology

The disadvantage of this technique is that it only analyzes the sensitivity from a single column, ignores the relationship between columns, and ignores the semantic information of each column.

[0007] To sum up, the existing sensitive quantification system has various problems such as incomplete definition of sensitive information and poor consideration of the relationship between columns. When faced with databases of different types, different semantic information, and different application scenarios, they cannot Accurately and quickly identify sensitive information and classify sensitivity

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