K-equivalence is a concept from the field of differential privacy, which is a framework for ensuring the privacy of individuals' data when it is being used for analysis or research. Specifically, K-equivalence refers to a privacy-preserving mechanism that ensures that the output of a function on a dataset remains similar (or "equivalent") when a single individual's data is added or removed from that dataset.
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