Secure Distributed Data Anonymization and Integration With m-Privacy

Provided by: Emory University
Topic: Big Data
Format: PDF
In this paper, the authors study 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) to infer the data records contributed by other data providers. The paper addresses this new threat, and makes several contributions. First, 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.

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