Anonymization is the process of hiding sensitive information from log entries to protect privacy. It involves removing or altering data in a way that prevents it from being linked to a specific person or entity. This is crucial for safeguarding individuals' privacy and ensuring compliance with data protection laws.
For example, let's say you have a list of customers' names and email addresses. Anonymization would involve replacing the names and email addresses with random codes or altering them in a way that it's impossible to identify who they belong to. This ensures that even if someone gets access to the data, they can't figure out who the individuals are.
Encryption secures data by making it unreadable without the proper decryption key, while anonymization focuses on removing or altering identifying information to make data anonymous.
Why is anonymization done?
Anonymization is often used in situations where the data needs to be used for analysis or research purposes, but the privacy of individuals must be protected. It allows organizations to share data without compromising the confidentiality of personal information. However, it's important to note that complete anonymization is not always possible, and there is always a risk of re-identification if enough data points are available.
One typical use-case is for machine learning models and for data science projects. These require user’s data to create valuable insights for the business to optimise operations and improve user conversion through deep understanding of user behaviour but they don’t need user’s personal information for it. So, to allow them the data to train, anonymization is done.
