Privacy preservation is one side of anonymization. The other side is retaining information so that the published data remains practically useful. There are broad categories of information metrics for measuring the data usefulness. A data metric measures the data quality in the entire anonymous table with respect to the data quality in the original table. A search metric guides each step of an anonymization (search) algorithm to identify an anonymous table with maximum information or minimum distortion. Often, this is achieved by ranking a set of possible anonymization operations and then greedily performing the “best” one at each step in the search. Since the anonymous table produced by a search metric is eventually evaluated by a data metric, the two types of metrics usually share the same principle of measuring data quality.