{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:29:35Z","timestamp":1790209775287,"version":"4.1.0"},"reference-count":63,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["New Media &amp; Society"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>This study analyzes the meanings and technical mechanisms of privacy that leading advertising technology (adtech) companies are deploying under the banner of \u201cprivacy-preserving\u201d adtech. We analyze this discourse by examining documents wherein Meta, Google, and Apple each propose to provide advertising attribution services\u2014which aim to measure and optimize advertising effectiveness\u2014while \u201csolving\u201d some of the privacy problems associated with online ad attribution. We find that these solutions define privacy primarily as anonymity, as limiting access to individuals\u2019 information, and as the prevention of third-party tracking. We critique these proposals by drawing on the theory of privacy as contextual integrity. Overall, we argue that these attribution solutions not only fail to achieve meaningful privacy but also leverage privacy rhetoric to advance commercial interests.<\/jats:p>","DOI":"10.1177\/14614448231213267","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T23:57:43Z","timestamp":1701215863000},"page":"2703-2724","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Private attributes: The meanings and mechanisms of \u201cprivacy-preserving\u201d adtech"],"prefix":"10.1177","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3157-2944","authenticated-orcid":false,"given":"Lee","family":"McGuigan","sequence":"first","affiliation":[{"name":"The University of North Carolina at Chapel Hill, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ido","family":"Sivan-Sevilla","sequence":"additional","affiliation":[{"name":"University of Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Parham","sequence":"additional","affiliation":[{"name":"University of Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Shvartzshnaider","sequence":"additional","affiliation":[{"name":"York University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,11,28]]},"reference":[{"key":"e_1_3_4_2_1","unstructured":"Apple (2021) Meet privacy-preserving ad attribution. 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