What is data anonymization, and what are its purposes?
Data anonymization is the process of transforming personal data in a way that makes it impossible to identify a specific person. It allows you to permanently remove the link between the information and the subjects it relates to. As a result, the data obtained and stored in the company is no longer considered personal data.
By using anonymization, each organization ensures the security of the data it manages. Thus, it meets the requirements of the GDPR and avoids the need to obtain consent for the processing of personal data. The anonymized information may also be used for purposes other than initially assumed. This form also allows you to store data for an unlimited time and export it abroad.
Data anonymization techniques
Various techniques can be used to anonymize data. Each of them modifies the data in a different way. Their diversity of possible options means that the selection of the appropriate method should depend on the specificity of a given situation - for example, on the industry in which the company operates or the type of information it manages. Some techniques can be used in combination.
The most important techniques for data anonymization include:
- Randomization - it's a random distribution of data in order to eliminate the close relationship between information and specific people. This method additionally uses disturbances (e.g., modification of values by a few points) and permutation, i.e., shuffling of attribute values in tables.
- Generalization - it's a deliberate reduction of data precision (e.g., by changing a specific value into a vector interval).
- Attribute Suppression - this technique deletes an entire batch of data in a set.
- Register Suppression - here, you delete the entire register in a data set. This technique affects many variables at once.
- Character Masking - it's all about changing the signs of a value, e.g., by using a fixed symbol.
- Pseudonymization - it's based on changing real values into fictitious ones. This method is similar to encrypting data that needs securely stored original values to be reread.
- Data perturbation - this technique uses substituting approximate values for real data.
- Synthesizing - it's a technique used primarily to generate synthetic datasets unrelated to the real dataset directly.
- Data aggregation - it's the conversion of the variable list into aggregated values.
How to anonymize data in the organization
Data anonymization brings many benefits to companies that use appropriate techniques. However, it is worth noting that this process is also associated with various complications. For example, it is difficult to use anonymized data in marketing activities or personalize a product or service. The information stored by the company becomes, to some extent, useless. Before deciding to anonymize a specific dataset, it is worth ensuring that the data you want to anonymize is not of particular value to your organization.
It is best to anonymize the data that is to be shared with third parties or stored only for archiving purposes. Various tools, such as Gallio , for example, can be used for this. Our tool allows you to anonymize images and videos in a quick and convenient way. Read more about automated video and image anonymization on our blog.