Toward the Cure of Privacy Policy Reading Phobia: Automated Generation of Privacy Nutrition Labels From Privacy Policies
Shidong Pan, Thong Hoang, Dawen Zhang, Zhenchang Xing, Xiwei Xu,, Qinghua Lu, and Mark Staples

TL;DR
This paper introduces an automated framework to generate privacy nutrition labels from privacy policies, helping users better understand data practices and addressing privacy policy reading phobia.
Contribution
It presents the first automated method for creating privacy nutrition labels from policies, achieving high F1-scores and detecting under-claim issues effectively.
Findings
Achieved 0.75 F1-score on data collection practices
Achieved 0.93 F1-score on security practices
Detected 90.1% of under-claim issues
Abstract
Software applications have become an omnipresent part of modern society. The consequent privacy policies of these applications play a significant role in informing customers how their personal information is collected, stored, and used. However, customers rarely read and often fail to understand privacy policies because of the ``Privacy Policy Reading Phobia'' (PPRP). To tackle this emerging challenge, we propose the first framework that can automatically generate privacy nutrition labels from privacy policies. Based on our ground truth applications about the Data Safety Report from the Google Play app store, our framework achieves a 0.75 F1-score on generating first-party data collection practices and an average of 0.93 F1-score on general security practices. We also analyse the inconsistencies between ground truth and curated privacy nutrition labels on the market, and our framework…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Privacy-Preserving Technologies in Data · Digital and Cyber Forensics
