Data Privacy-Enhancement Services protect sensitive data by employing Privacy-Enhancing Technologies (PETs), ensuring secure data use without compromising privacy. These services allow organizations to manage and protect sensitive data and facilitate safe data sharing and collaboration while complying with regulatory standards without compromising its analytical value. Key Features: Tokenization: Sensitive data elements (e.g., credit card numbers, PII) are replaced with token values, keeping the original data safe. Tokenized data can be used in applications without exposing actual sensitive values. This feature is widely used in industries like finance and healthcare to reduce data exposure risks while enabling secure transactions. Anonymization and K-anonymization: These services remove or obfuscate personally identifiable information (PII), making it impossible to trace the data back to individuals. K-anonymization provides an added layer by ensuring that data records are indistinguishable among 'k' individuals, minimizing re-identification risks in large datasets, especially in data-sharing scenarios. Differential Privacy: Differential privacy introduces noise to the data to mask individual entries while still enabling useful statistical analysis. Global differential privacy is applied at the dataset level, while local differential privacy applies noise to data points before they are collected, offering enhanced security for sensitive data at the collection stage.