{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T05:02:36Z","timestamp":1698037356321},"reference-count":0,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":"Data protection regulations in many countries require IT systems to implement baseline privacy requirements like purpose limitation and consent as mandated by the GDPR. Such requirements are often specified in the system\u2019s privacy policy and are challenging to implement as system developers must address them consistently and in a cross-cutting manner. Moreover, without a formal connection between a system\u2019s privacy policy and its implementation, the system\u2019s correctness and evolution are extremely difficult to attain. We propose a model-driven development methodology that incorporates privacy policies into the system design. Namely, we define a system\u2019s privacy model, which has precise semantics and is used to specify privacy policies. We provide semantic-preserving model transformations that generate system implementations that enforce the given privacy policies by design. We implement two such model transformations, targeting C# and Python system implementations. We evaluate our methodology on three substantial case studies and show the enforcement of privacy policies related to purpose limitation and consent. Our evaluation also demonstrates our approach\u2019s generality, effectiveness, and modest overhead.<\/jats:p>","DOI":"10.56553\/popets-2024-0018","type":"journal-article","created":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T21:01:15Z","timestamp":1698008475000},"page":"314-329","source":"Crossref","is-referenced-by-count":0,"title":["Model-driven Privacy"],"prefix":"10.56553","volume":"2024","author":[{"given":"Srdan","family":"Krstic","sequence":"first","affiliation":[{"name":"ETH Zurich"}]},{"given":"Hoang","family":"Nguyen","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]},{"given":"David","family":"Basin","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]}],"member":"35752","published-online":{"date-parts":[[2024,1]]},"container-title":["Proceedings on Privacy Enhancing Technologies"],"original-title":[],"deposited":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T21:01:39Z","timestamp":1698008499000},"score":1,"resource":{"primary":{"URL":"https:\/\/petsymposium.org\/popets\/2024\/popets-2024-0018.php"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["10.56553\/popets-2024-0018"],"URL":"https:\/\/doi.org\/10.56553\/popets-2024-0018","relation":{},"ISSN":["2299-0984"],"issn-type":[{"value":"2299-0984","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}