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Honesty is the Best Policy: On the Accuracy of Apple Privacy Labels Compared to Apps' Privacy Policies

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arxiv 2306.17063 v2 pith:UE6PGNLI submitted 2023-06-29 cs.CR

classification cs.CR
keywords privacylabelsappsappledeveloperspolicydatapolicies
verification ladder T0 review T1 audit T2 compute T3 formal

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Apple introduced privacy labels in Dec. 2020 as a way for developers to report the privacy behaviors of their apps. While Apple does not validate labels, they also require developers to provide a privacy policy, which offers an important comparison point. In this paper, we fine-tuned BERT-based language models to extract privacy policy features for 474,669 apps on the iOS App Store, comparing the output to the privacy labels. We identify discrepancies between the policies and the labels, particularly as they relate to data collected linked to users. We find that 228K apps' privacy policies may indicate data collection linked to users than what is reported in the privacy labels. More alarming, a large number (97%) of the apps with a Data Not Collected privacy label have a privacy policy indicating otherwise. We provide insights into potential sources for discrepancies, including the use of templates and confusion around Apple's definitions and requirements. These results suggest that significant work is still needed to help developers more accurately label their apps. Our system can be incorporated as a first-order check to inform developers when privacy labels are possibly misapplied.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SoK: From Generation to Consumption of Privacy Documents in Software Systems

    cs.CR 2026-08 conditional novelty 6.0 of 10

    A systematic review of 290 papers (2010 to 2025) organizes privacy-document research into a five-stage lifecycle and identifies 15 trends, 21 opportunities, and 4 research directions.

  2. Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents

    cs.CR 2025-08 conditional novelty 6.0 of 10

    A new 7,138-scenario benchmark shows mainstream smartphone AI agents frequently fail to notice or warn about private information.

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