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Masking with k-anonymization is the best policy to use in this scenario, as it prevents individuals in a dataset from being associated with quasi-identifiers.
Once this policy is applied to a data source, Immuta examines pairs of values across columns and hides groups that do not appear at least the specified number of times (k). For example, if one column contains street numbers and another contains street names, the group 123, “Main Street” probably would appear frequently while the group 123, “Diamondback Drive” probably would show up much less. Since the second group appears infrequently, the values could potentially identify someone, so this group would be masked.
Which Data Policy would you suggest? Type your response below.
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They need a masking policy that prevents quasi-identifiers from revealing who an individual is.