{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:45:58Z","timestamp":1760060758278,"version":"build-2065373602"},"reference-count":76,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSERC Discovery","award":["RGPIN-2015-06048"],"award-info":[{"award-number":["RGPIN-2015-06048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cryptography"],"abstract":"<jats:p>Digital contact-tracing (CT) systems differ in how they process risk and expose data, and the centralized\u2013decentralized dichotomy obscures these choices. We propose a modular six-model classification and evaluate 18 platforms across 12 countries (July 2020\u2013April 2021) using a 24-indicator rubric spanning privacy, security, functionality, and governance. Methods include double-coding with Cohen\u2019s \u03ba for inter-rater agreement and a 1000-draw weight-sensitivity check; assumptions and adversaries are stated in a concise threat model. Results: No single model dominates; Bulletin Board and Custodian consistently form the top tier on privacy goals, while Fully Centralized eases verification\/notification workflows. Timelines show rapid GAEN uptake and near-contemporaneous open-source releases, with one late outlier. Contributions: (i) A practical, generalizable classification that makes compute-locus and data addressability explicit; (ii) a transparent indicator rubric with an evidence index enabling traceable scoring; and (iii) empirically grounded guidance aligning deployments with goals G1\u2013G3 (PII secrecy, notification authenticity, unlinkability). Limitations include reliance on public documentation and architecture-level (not mechanized) verification; future work targets formal proofs and expanded double-coding. The framework and findings generalize beyond COVID-19 to privacy-preserving digital-health workflows.<\/jats:p>","DOI":"10.3390\/cryptography9040060","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T13:16:11Z","timestamp":1758719771000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Driven Classification of Contact Tracing Platforms: Architecture and Adoption Insights"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3520-7373","authenticated-orcid":false,"given":"Sidra","family":"Anwar","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Memorial University of Newfoundland, St John\u2019s, NL A1C 5S7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7352-6463","authenticated-orcid":false,"given":"Jonathan","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Memorial University of Newfoundland, St John\u2019s, NL A1C 5S7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4099","DOI":"10.1177\/14614448221109800","article-title":"Between surveillance and technological solutionism: A critique of privacy-preserving apps for COVID-19 contact-tracing","volume":"26","author":"Mann","year":"2024","journal-title":"New Media Soc."},{"key":"ref_2","unstructured":"Gvili, Y. 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