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Privacy in data publishing has received much attention recently. The key to defining privacy is to model knowledge of the attacker-if the attacker is assumed to know too little, the published data can be easily attacked, if the attacker is assumed to know too much, the published data has little utility. Previous work considered either quite ignorant adversaries or nearly omniscient adversaries. In this paper, the authors introduce a new class of adversaries that they call realistic adversaries who live in the unexplored space in between. Realistic adversaries have knowledge from external sources with an associated stubbornness indicating the strength of their knowledge.
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