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Abstract
Newly published data, when combined with existing public knowledge, allows for complex and sometimes unintended inferences. We propose semi-automated tools for detecting these inferences prior to releasing data. Our tools give data owners a fuller understanding of the implications of releasing data and help them adjust the amount of data they release to avoid unwanted inferences. Our tools first extract salient keywords from the private data intended for release. Then, they issue search queries for documents that match subsets of these keywords, within a reference corpus (such as the public Web) that encapsulates as much of relevant public knowledge as possible. Finally, our tools parse the documents returned by the search queries for keywords not present in the original private data. These additional keywords allow us to automatically estimate the likelihood of certain inferences. Potentially dangerous inferences are flagged for manual review. We call this new technology Web-based inference control. The paper reports on two experiments which demonstrate early successes of this technology. The first experiment shows the use of our tools to automatically estimate the risk that an anonymous document allows for re-identification of its author. The second experiment shows the use of our tools to detect the risk that a document is linked to a sensitive topic. These experiments, while simple, capture the full complexity of inference detection and illustrate the power of our approach. 1
Keyphrases
search query new technology web-based inference control sensitive topic full complexity manual review relevant public knowledge extract salient keywords dangerous inference second experiment private data early success paper report public knowledge unwanted inference first experiment public web inference detection fuller understanding anonymous document data owner reference corpus original private data unintended inference semi-automated tool certain inference additional keywords
