Anonymization is the process of using data while preserving privacy. It is a key method for sharing data while minimizing privacy risks. This review discusses modern approaches to anonymization, including traditional de-identification techniques and modern methods like data query systems, synthetic data, and differential privacy. It highlights the challenges of anonymization in the age of big data and the need for a balance between privacy and utility. Traditional de-identification techniques often fail to provide a good privacy-utility trade-off for modern data. Aggregate data, such as synthetic data and data query systems, can offer better trade-offs but are not inherently protected against privacy attacks. The review emphasizes the importance of combining formal methods with empirical evaluation of robustness against attacks. It also discusses the legal definitions of anonymous data and the debate between formalists and pragmatists in the field. The review concludes that differential privacy is a promising approach for releasing aggregate data, and that future research should focus on improving privacy-utility trade-offs. The review also highlights the vulnerability of aggregate data to privacy attacks, including membership inference, attribute inference, and reconstruction attacks. The review concludes that while aggregate data is generally less vulnerable to attacks than record-level data, it can still leak information about individual records. The review emphasizes the need for careful design and auditing of anonymization techniques to ensure privacy and utility.Anonymization is the process of using data while preserving privacy. It is a key method for sharing data while minimizing privacy risks. This review discusses modern approaches to anonymization, including traditional de-identification techniques and modern methods like data query systems, synthetic data, and differential privacy. It highlights the challenges of anonymization in the age of big data and the need for a balance between privacy and utility. Traditional de-identification techniques often fail to provide a good privacy-utility trade-off for modern data. Aggregate data, such as synthetic data and data query systems, can offer better trade-offs but are not inherently protected against privacy attacks. The review emphasizes the importance of combining formal methods with empirical evaluation of robustness against attacks. It also discusses the legal definitions of anonymous data and the debate between formalists and pragmatists in the field. The review concludes that differential privacy is a promising approach for releasing aggregate data, and that future research should focus on improving privacy-utility trade-offs. The review also highlights the vulnerability of aggregate data to privacy attacks, including membership inference, attribute inference, and reconstruction attacks. The review concludes that while aggregate data is generally less vulnerable to attacks than record-level data, it can still leak information about individual records. The review emphasizes the need for careful design and auditing of anonymization techniques to ensure privacy and utility.