Detecting Privacy Leaks Using Corpus-based Association RulesAuthors: Richard Chow, Philippe Golle and Jessica Staddon
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 analogues 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.