ATLAS: Automatically Detecting Discrepancies Between Privacy Policies
  and Privacy Labels
Akshath Jain David Rodriguez Jose M. del Alamo Norman Sadeh
Privacy policies are long, complex documents that end-users seldom read.
Privacy labels aim to ameliorate these issues by providing succinct summaries
of salient data practices. In December 2020, Apple began requiring that app
developers submit privacy labels describing their apps' data practices. Yet,
research suggests that app developers often struggle to do so. In this paper,
we automatically identify possible discrepancies between mobile app privacy
policies and their privacy labels. Such discrepancies could be indicators of
potential privacy compliance issues.
  We introduce the Automated Privacy Label Analysis System (ATLAS). ATLAS
includes three components: a pipeline to systematically retrieve iOS App Store
listings and privacy policies; an ensemble-based classifier capable of
predicting privacy labels from the text of privacy policies with 91.3% accuracy
using state-of-the-art NLP techniques; and a discrepancy analysis mechanism
that enables a large-scale privacy analysis of the iOS App Store.
  Our system has enabled us to analyze 354,725 iOS apps. We find several
interesting trends. For example, only 40.3% of apps in the App Store provide
easily accessible privacy policies, and only 29.6% of apps provide both
accessible privacy policies and privacy labels. Among apps that provide both,
88.0% have at least one possible discrepancy between the text of their privacy
policy and their privacy label, which could be indicative of a potential
compliance issue. We find that, on average, apps have 5.32 such potential
compliance issues.
  We hope that ATLAS will help app developers, researchers, regulators, and
mobile app stores alike. For example, app developers could use our classifier
to check for discrepancies between their privacy policies and privacy labels,
and regulators could use our system to help review apps at scale for potential
compliance issues.