{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:31:58Z","timestamp":1760711518353,"version":"3.38.0"},"reference-count":0,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PoPETs"],"abstract":"<jats:p>Identifying contextual integrity (CI) and governing knowledge commons (GKC) parameters in privacy policy texts can facilitate normative privacy analysis. However, GKC-CI annotation has heretofore required manual or crowdsourced effort. This paper demonstrates that high-accuracy GKC-CI parameter annotation of privacy policies can be performed automatically using large language models. We fine-tune 50 open-source and proprietary models on 21,588 ground truth GKC-CI annotations from 16 privacy policies. Our best performing model has an accuracy of 90.65%, which is comparable to the accuracy of experts on the same task. We apply our best performing model to 456 privacy policies from a variety of online services, demonstrating the effectiveness of scaling GKC-CI annotation for privacy policy exploration and analysis. We publicly release our model training code, training and testing data, an annotation visualizer, and all annotated policies for future GKC-CI research.<\/jats:p>","DOI":"10.56553\/popets-2025-0062","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T23:36:18Z","timestamp":1741390578000},"page":"280-308","source":"Crossref","is-referenced-by-count":1,"title":["Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models"],"prefix":"10.56553","volume":"2025","author":[{"given":"Jake","family":"Chanenson","sequence":"first","affiliation":[{"name":"University of Chicago"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madison","family":"Pickering","sequence":"additional","affiliation":[{"name":"University of Chicago"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noah","family":"Apthrope","sequence":"additional","affiliation":[{"name":"Colgate University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"35752","published-online":{"date-parts":[[2025,4]]},"container-title":["Proceedings on Privacy Enhancing Technologies"],"original-title":[],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T23:36:40Z","timestamp":1741390600000},"score":1,"resource":{"primary":{"URL":"https:\/\/petsymposium.org\/popets\/2025\/popets-2025-0062.php"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["10.56553\/popets-2025-0062"],"URL":"https:\/\/doi.org\/10.56553\/popets-2025-0062","relation":{},"ISSN":["2299-0984"],"issn-type":[{"value":"2299-0984","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}