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We consider group-based anonymization schemes, 1 a popular approach to data publishing. This approach aims 2 at protecting privacy of the individuals involved in a dataset, 3 by releasing an obfuscated version of the original data, where 4 the exact correspondence between individuals and attribute 5 values is hidden. When publishing data about individuals, one 6 must typically balance the learner's utility against the risk 7 posed by an attacker, potentially targeting individuals in the 8doi:10.1109/tifs.2019.2937640 fatcat:oketvtntrvde7hfj2lroufoyma