Steered Microaggregation: A Unified Primitive for Anonymization of Data Sets and Data Streams

Abstract

The data anonymization landscape has become quite complex in the last decades. On the methodology side, the statistical disclosure control methods designed in official statistics have been supplemented by a number of privacy models proposed by computer scientists. On the data side, static data sets now coexist with big data, and particularly data streams. In the quest for a unified and conceptually simple anonymization approach, we present here a primitive called steered microaggregation that can be tailored to enforce various privacy models both on static data sets and also on data streams. This type of microaggregation relies on adding artificial attributes that are properly initialized and weighted in order to steer the microaggregation process into meeting certain desired constraints. Although not limited to these, we demonstrate the potential of steered microaggregation by showing how it can be used to achieve t-closeness in the context of static data sets and to achieve k-anonymity of data streams while controlling tuple reordering.