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Apple introduced \textit{privacy labels} in Dec. 2020 as a way for developersto report the privacy behaviors of their apps. While Apple does not validatelabels, they do also require developers to provide a privacy policy, whichoffers an important comparison point. In this paper, we applied the NLPframework of Polisis to extract features of the privacy policy for 515,920 appson the iOS App Store comparing the output to the privacy labels. We identifydiscrepancies between the policies and the labels, particularly as it relatesto data collected that is linked to users. We find that 287$\pm196$K apps'privacy policies may indicate data collection that is linked to users than whatis reported in the privacy labels. More alarming, a large number of(97$\pm30$\%) of the apps that have {\em Data Not Collected} privacy label havea privacy policy that indicates otherwise. We provide insights into potentialsources for discrepancies, including the use of templates and confusion aroundApple's definitions and requirements. These results suggest that there is stillsignificant work to be done to help developers more accurately labeling theirapps. Incorporating a Polisis-like system as a first-order check can helpimprove the current state and better inform developers when there are possiblemisapplication of privacy labels.

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