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Understanding privacy risk with k-anonymity and l-diversity
Learn how to protect privacy in datasets using k-anonymity and l-diversity.
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. K-anonymity is a data anonymization technique ensuring that for each combination of quasi-identifying attributes (such as country and tenure), there are at least k rows that share those exact values. K-anonymity and l-diversity are two data anonymization techniques that can help you reason and make conscious decisions about the privacy risks for a dataset.
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