Recently, I came across a term called ‘anonymized data’. I did little research on Google and found that it is used for data which removes or hides away the credentials associated with it. Like during school or college days we used to have poems and stories written by anonymous. Similarly, datasets are made nameless so that they can be used for research and other related purposes.
Generally, there are some personal identifiers in datasets like name, caste, creed, address, age, salary, etc. So, this kind of data if used directly will be like preaching privacy or stealing details. Thus, to make it holistic and useful for all, these identifiers are removed or made blur or changed into scribbles so that this data can be used by many parties at a time. This is also called masking the data. Having made data anonymized sets the users and parties free from copyright and privacy issues. There are many ways to make data anonymized, for example with editing tools or Photoshop and so on.
And in the case of text data, to make it anonymous, owners can delete some key variables or use some other pseudonyms to hide the real identity. In image and video data, blurring is the most used technique. Anonymized data is a part of data cleansing and preparation and analysis. Companies into data cleansing services often convert data laced with identifiers into anonymous form.