  Privacy policies are the primary channel through which companies inform users
about their data collection and sharing practices. These policies are often
long and difficult to comprehend. Short notices based on information extracted
from privacy policies have been shown to be useful but face a significant
scalability hurdle, given the number of policies and their evolution over time.
Companies, users, researchers, and regulators still lack usable and scalable
tools to cope with the breadth and depth of privacy policies. To address these
hurdles, we propose an automated framework for privacy policy analysis
(Polisis). It enables scalable, dynamic, and multi-dimensional queries on
natural language privacy policies. At the core of Polisis is a privacy-centric
language model, built with 130K privacy policies, and a novel hierarchy of
neural-network classifiers that accounts for both high-level aspects and
fine-grained details of privacy practices. We demonstrate Polisis' modularity
and utility with two applications supporting structured and free-form querying.
The structured querying application is the automated assignment of privacy
icons from privacy policies. With Polisis, we can achieve an accuracy of 88.4%
on this task. The second application, PriBot, is the first freeform
question-answering system for privacy policies. We show that PriBot can produce
a correct answer among its top-3 results for 82% of the test questions. Using
an MTurk user study with 700 participants, we show that at least one of
PriBot's top-3 answers is relevant to users for 89% of the test questions.
