A lot of our personal data is collected by the websites we visit, the forms we fill out over the internet, the platforms where we register. Because we share this data with our consent, website owners may collect and store it.
When we can personalize our cookie settings and set the limits of the personal data we share, it helps us to know the period in which this data will be stored and the date on which it will be deleted and to be informed about it. So, what is the erasure of the personal data we share and anonymize the personal data?
Anonymization of personal data is defined under the law as “making personal data in no way be associated with a specific or identifiable natural person, even if it is matched with other data”.
Since the data anonymized for the purpose of anonymization of personal data will no longer have the character of personal data, it will not be considered and enforced under the law.
In detail, anonymization of personal data is the prevention of identification of the person concerned by removing or changing all direct and indirect identifiers contained in a data set. Thus, the data will have lost the characteristic of being distinguishable within a crowd and group in such a way that it cannot be associated with a real person.
If a trace is made on the data, it can be understood who the data belongs to, if a match is reached, this personal data cannot be called anonymized. Methods of anonymization of personal data
Data controllers decide which method to use based on the data in their hands. When making this decision, it takes into account aspects such as the nature, size of the data, the nature of its presence in physical environments, the variety, the frequency of processing, the reliability of the party to be transferred, the magnitude of the damage that will occur in case of deterioration of anonymity.
Let us continue our article by examining the four main methods of anonymization of personal data;
1. Masking: is a method of destroying certain areas of personal data, rendering the data owner unidentifiable. For example, masking is available if a person's ID number or credit card number is deleted/starched. (8896****** 1107)
2. Aggregation/Cumulative data creation: It is the integration of personal data and the reflection of aggregate values. For example, the number of female employees in a company is x, and the data on 56% of them are graduates of a bachelor's degree is anonymized.
3. Data derivation: is the replacement of the data contained in more detail in the present with their more general counterparts. An example is to write the person's age instead of writing the day, month, year details of the person's birthday. If another example is given, anonymization can be made by giving a specific interval instead of writing down the person's salary information in detail.
4. Data hash: is to destroy the detectability of individuals without harming the aggregate utility by mixing and shifting values within a dataset. The method of data mixing was used if the values indicating the age of the people were randomly interchanged with each other in a company where the age was to be averaged.
Disclaimer: All rights to any articles and content published belong to Efilli Software. All or part of any content, such as text, audio, video, and even if the source is shown or the active link is provided, cannot be used, published, shared or modified.
