Understanding privacy risk with k-anonymity and l-diversity
(marcusolsson.dev)
Imagine you’re a data analyst at a global company who’s been asked to provide employee statistics for a survey on remote working and distributed teams. You’ve extracted the relevant employee data, but sharing it as-is could violate privacy laws. How can you anonymize this data while ensuring it’s still useful? In this article, you’ll learn about k-anonymity and l-diversity—two valuable techniques in privacy engineering to help you reduce the privacy risk in datasets.
Imagine you’re a data analyst at a global company who’s been asked to provide employee statistics for a survey on remote working and distributed teams. You’ve extracted the relevant employee data, but sharing it as-is could violate privacy laws. How can you anonymize this data while ensuring it’s still useful? In this article, you’ll learn about k-anonymity and l-diversity—two valuable techniques in privacy engineering to help you reduce the privacy risk in datasets.