Institute of Electrical & Electronic Engineers
In this paper, the authors consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. They consider a new type of \"Insider attack\" by colluding data providers who may use their own data records (a subset of the overall data) in addition to the external background knowledge to infer the data records contributed by other data providers. The paper addresses this new threat and makes several contributions. They introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m colluding data providers.