Anonymization techniques are methods used to protect personal data by removing or altering identifiable information. This ensures that individuals cannot be easily recognized from the data, which is crucial for maintaining privacy. Common techniques include data masking, where sensitive information is replaced with fictional data, and k-anonymity, which ensures that any given data point is indistinguishable from at least k other data points. Another popular method is differential privacy, which adds random noise to datasets, making it difficult to identify specific individuals while still allowing for useful analysis. These techniques are essential in fields like healthcare and finance, where sensitive information must be handled responsibly.