We survey the state of the art of privacy in perturbative methods for statistical disclosure control. While the focus is on data microaggregation, these methods also address a wide variety of alternative applications such as obfuscation in location-based services. More specifically, we examine – anonymity and some of its enhancements. Motivated by the vulnerability of these measures to similarity and skewness attacks, we compare three recent criteria for privacy based on information-theoretic concepts that attempt to circumvent this vulnerability.