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Studies have shown website privacy policies are too long and hard to comprehend for their target audience. These studies and a more recent body of research that utilizes machine learning and natural language processing to automatically summarize privacy policies greatly benefit, if not rely on, corpora of privacy policies collected from the web. While there have been smaller annotated corpora of web privacy policies made public, we are not aware of any large publicly available corpus. We use DMOZ, a massive open-content directory of the web, and its manually categorized 1.5 million web-sites, to collect hundreds of thousands of privacy policies associated with their categories, enabling research on privacy policies across different categories/market sectors. We review statistics of this cor-pus and make it available for research. We also obtain valuable insights about privacy policies, e.g., which websites post them less often. Our corpus of web privacy policies is a valuable tool at the researchers’ disposal to investigate privacy policies. For example, it facilitates comparison among different methods of privacy pol-icy summarization by providing a benchmark, and can be used in unsupervised machine learning to automatically digest privacy policies.
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