document: Detecting privacy leaks using corpus-based association rules Detecting inferences in documents is critical for ensuring privacy when sharing information . In this paper , we propose a refined and practical model of inference detection using a reference corpus . Our model is inspired by association rule mining : inferences are based on word co-occurrences . Using the model and taking the Web as the reference corpus , we can find inferences and measure their strength through web-mining algorithms that leverage search engines such as Google or Yahoo ! . Our model also includes the important case of private corpora , to model inference detection in enterprise settings in which there is a large private document repository . We find inferences in private corpora by using analogs of our Web-mining algorithms , relying on an index for the corpus rather than a Web search engine . We present results from two experiments . The first experiment demonstrates the performance of our techniques in identifying all the keywords that allow for inference of a particular topic ( e.g. `` HIV '' ) with confidence above a certain threshold . The second experiment uses the public Enron e-mail dataset . We postulate a sensitive topic and use the Enron corpus and the Web together to find inferences for the topic . These experiments demonstrate that our techniques are practical , and that our model of inference based on word co-occurrence is well-suited to efficient inference detection .
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