Which method is commonly used to ensure data privacy in cloud computing?

Tokenization is a widely utilized method to ensure data privacy in cloud computing. It replaces sensitive data elements, such as personal identification numbers or credit card details, with non-sensitive equivalents known as tokens. These tokens have no intrinsic value and cannot be reverse-engineered to expose the original sensitive data, thereby significantly minimizing the risk of data breaches and unauthorized access.

In cloud environments, where data is often stored and processed across multiple locations, tokenization enhances security by allowing organizations to protect sensitive information even when it is shared with third parties or external systems. This method not only safeguards data effectively but also helps organizations comply with regulatory requirements, such as GDPR or PCI DSS, which mandate strict controls over sensitive information.

Additional methods such as data mining, network segmentation, and direct access control address various aspects of data management and security but do not specifically enhance data privacy to the extent that tokenization does. Data mining focuses on analyzing and extracting useful patterns from large sets of data, while network segmentation organizes the infrastructure to improve security and performance. Direct access control is about managing users' access to data rather than masking sensitive information, making tokenization the most effective method for ensuring data privacy in the context of cloud computing.