What is Data Anonymization?

Data anonymization is the process of mitigating direct and indirect privacy risks within data to ensure records cannot be attributed to a specific individual or entity. With increasing reliance on data for applications, machine learning models, and AI technologies, effective anonymization techniques are crucial. The three main tenets of data anonymization include policy-based de-identification, direct privacy risk mitigation, and evaluation for indirect risks. Various techniques can be used to anonymize data, such as data removal and reduction, entity replacement, numerical and date shifting, synthetic data generation, and encryption or tokenization. Anonymized data has numerous use cases across industries like healthcare, finance, government, education, digital media, and public utilities.