Why are data cleansing and anonymization important tools in information management?

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Reducing the risk of personal information being stolen or misappropriated in your organization through database cleansing and de-identification processes is an integral part of properly direct mail mortgage marketing list exercising personal data protection in your organization. This process, which requires a plan, implementation, periodic monitoring and the promotion of a data protection culture, will not only minimize the chances of information leakage but will also assist in administrative decision-making by protecting data owners, regardless of whether they are customers, suppliers, contractors, partners or collaborators.

But here someone will ask: What is de-identification? Well, it is nothing more than the process of eliminating any information that fully identifies an individual or that can reasonably be used to achieve his or her identification.



De-identification is quite useful with long-term data storage; it can be understood by observing processes in different organizations such as those of a residential complex whose administrative management keeps records detailing personal data, payments, financial contributions and other relevant information of each co-owner; however, sometimes that co-owner ceases to be one and therefore, his or her personal data in particular is no longer used, so then, from now on only the record of payments, contributions or other accounting data linked to an identification number and certain public data of the former co-owner should be kept.

From the above, the information system that houses the databases must be subjected to a manual or automatic de-identification process. This process will not only protect the privacy of the owners by minimizing the risk of their data leakage, but will also simplify the databases themselves, since it will preserve only what is truly useful.

The number of times data cleansing and de-identification is performed is determined by a systematic analysis of the level and types of re-identification risk, which is performed as follows:

Classifying the data within each record. Just because a data set relates to an individual does not mean that all of its fields are identifiable. Both cleaning and de-identification only deal with variables that can be used to identify individuals. For example, a custom survey database can be de-identified by removing personal data and leaving the relevant segments, leaving only consumer habits by age group, geographic area, purchasing power, gender, etc.


Determining a re-identification risk threshold. The higher the risk of re-identification of a data set, the greater the amount of de-identification required. To do this, you must evaluate to what extent disclosure would invade the privacy of a data subject. A very common example is presented by a person who accesses a negative report due to a late payment. However, when the debt is paid and the sanction period is exceeded, the data subject is de-identified from the credit bureau databases, and his negative history disappears. If this did not happen, although he would now have a high rating in the credit analysis, the data subject would be harmed since his negative history would continue to stigmatize him.
Measuring the risks of data theft and its potential to re-identify individuals . Risk is an expression of probability indicating whether one or more fields in a data record allow individuals to be re-identified, so that a security attack resulting in theft of information will allow the abductor to identify one or more individuals in the data set. The analysis considers the motivation, the attacker's capability, the controls and the security of both the storage site and the data exchange processes.

[yadaysrdone]

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