How does Data Anonymization works?

Data anonymization involves manipulating the dataset in such a way that it eliminates or otherwise changes personal data, making it impossible to track a particular subject with surety. Here’s a closer look at the two main approaches:

• Removing PII: This means full exclusion of certain pieces of information that are potentially used to directly remove the identity of an individual. Here are some common methods:Here are some common methods:
  • Data deletion: This is the most straightforward method where it sees the entire fields that contain names, addresses, and social security numbers removed from the dataset.
  • Truncation: This method involves censoring a part of a field in a PII. As a case in point the last four digits of a social security number may be deleted.
• Modifying PII: What can be done instead of and instead of complete removal of PII it is possible to obfuscate personal information in such a way as to ensure data usability. Here are some techniques:
  • Masking: In this data, PII entries are replaced by fake data while PII values are left intact. This may include replacing names with alien characters and or exchanging the phone numbers to a specific pattern like ###. g. dataring (or with all names beginning with 555), or simply encoding addresses by zip code only.
  • Generalization: This entails that the specific details are expanded so that they can be grouped under a given category. For instance, ages can be converted into age groups (20-30, 31-40), or postal/zip code numbers can be divided into more general regions.
  • Aggregation: Some data is aggregated while it is grouped into more entities. For instance, purchase history for these customers could be digitized but not customer-specific data; instead, summary information – the number of these products sold for a specific category during a given period.

With extensive data collection, protecting individual privacy while harnessing the power of data for analytics has become a paramount concern.

In this article we will explore the process of Data Anonymization, which serves as a vital solution to strike this delicate balance.