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What is big data desensitization? In view of this problem, this article introduces the corresponding analysis and answers in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible way.

Big data data desensitization, also known as data bleaching, data de-privacy or data deformation, refers to the deformation of some sensitive information through desensitization rules to achieve reliable protection of sensitive private data. in this way, desensitized real data sets can be safely used in development, testing and other non-production environments as well as outsourced environments.

Private data desensitization technology

Usually in the big data platform, the data is stored in a structured format, each table is composed of many rows, and each row of data is composed of many columns. Depending on the data properties of the column, data columns can usually be divided into the following types:

A column that can pinpoint a person is called an identifiable column, such as ID number, address, and name.

A single column cannot locate an individual, but multiple columns of information can be used to potentially identify a person. these columns are called semi-identifying columns, such as zip code, birthday, gender, etc. According to a US research paper, 87% of Americans can be identified by using only zip code, birthday and gender information [3].

Columns that contain sensitive information about users, such as transaction volume, illness, and income.

Other columns that do not contain user sensitive information.

The so-called avoidance of private data disclosure refers to the people who avoid using the data (data analysts, BI engineers, etc.) to identify a row of data as someone's information. Data desensitization technology through desensitization of data, such as removing identification columns, converting semi-identification columns and so on, on the basis of ensuring that data users can analyze the data of # 2 (converted) semi-identification column, # 3 sensitive information column and # 4 other columns, to a certain extent, they can not identify users according to the data, so as to achieve a balance between ensuring data security and maximizing the value of mining data.

Privacy data disclosure type

Privacy data disclosure can be divided into many types, according to different types, we can usually use different privacy data disclosure risk models to measure the risk of preventing privacy data disclosure, and corresponding to different data desensitization algorithms to desensitize data. In general, the types of privacy data disclosure include:

Personal identification leaked. When the data user confirms in any way that a piece of data in the data table belongs to someone, it is called personal identity disclosure. Personal identification disclosure is the most serious, because in the event of personal identification disclosure, data users can get sensitive information about specific individuals.

Attribute disclosure is called attribute disclosure when data users learn about someone's new attribute information according to the data table they access. Personal identity disclosure will certainly lead to attribute disclosure, but attribute disclosure can also occur separately.

Membership leaked. When the data user can confirm that someone's data exists in the data table, it is called membership disclosure. The relative risk of membership disclosure is relatively small, personal identification disclosure and attribute disclosure must mean membership disclosure, but membership disclosure may also occur separately.

Risk model of privacy data disclosure

Open data to data analysts while introducing the risk of private data disclosure. While limiting the risk of private data disclosure within a certain range, maximizing the potential of data analysis and mining is the ultimate goal of data desensitization technology. At present, in the field of private data desensitization, there are several different models that can be used to measure the possible risk of privacy data disclosure from different angles.

This is the end of the answer to big data's desensitization question. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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