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.

我们提出了一个基于 Polisis 的自动化框架，能够在隐私政策的众多细节方面进行可扩展的、动态的、多维查询。最初的 Polisis 具有一个隐私中心语言模型和神经网络分类器的新等级结构，支持由结构化和自由形式查询支持的两个应用程序：自动分配隐私图标和 PriBot 隐私答案查询。