anonymization algorithms

Anonymization algorithms are methods used to protect personal information in datasets by removing or altering details that could identify individuals. Techniques include removing names and social security numbers, replacing specific data with generic values, or generalizing details—such as turning exact ages into age ranges. These algorithms aim to balance privacy with data utility, ensuring that sensitive information is protected while the data remains useful for analysis or research. The goal is to prevent the identification of individuals while still allowing meaningful insights from the data.