{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T08:58:28Z","timestamp":1774601908407,"version":"3.50.1"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"2s","license":[{"start":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T00:00:00Z","timestamp":1623628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"PETRAS2: National Centre of Excellence for IoT Systems Cyber Security","award":["EP\/S035362\/1"],"award-info":[{"award-number":["EP\/S035362\/1"]}]},{"name":"Quarriable Smart City Data Markets","award":["EP\/T517203\/1"],"award-info":[{"award-number":["EP\/T517203\/1"]}]},{"name":"PACE: Privacy-aware Cloud Ecosystems","award":["EP\/R033439\/1"],"award-info":[{"award-number":["EP\/R033439\/1"]}]},{"name":"The Research Council (TRC), Sultanate of Oman","award":["BFP\/RGP\/ICT\/19\/186"],"award-info":[{"award-number":["BFP\/RGP\/ICT\/19\/186"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,6,21]]},"abstract":"<jats:p>\n            Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This article lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context\u2014this approach is defined as\n            <jats:italic>\u201cExplainable Privacy.\u201d<\/jats:italic>\n            A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in Reference [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this article identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers\u2019 capabilities at the design phase.\n          <\/jats:p>","DOI":"10.1145\/3434186","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T12:55:42Z","timestamp":1623675342000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Synthesising Privacy by Design Knowledge Toward Explainable Internet of Things Application Designing in Healthcare"],"prefix":"10.1145","volume":"17","author":[{"given":"Lamya","family":"Alkhariji","sequence":"first","affiliation":[{"name":"Cardiff University, UK"}]},{"given":"Nada","family":"Alhirabi","sequence":"additional","affiliation":[{"name":"Cardiff University, UK"}]},{"given":"Mansour Naser","family":"Alraja","sequence":"additional","affiliation":[{"name":"Dhofar University, Oman"}]},{"given":"Mahmoud","family":"Barhamgi","sequence":"additional","affiliation":[{"name":"Claude Bernard Lyon 1 University, France"}]},{"given":"Omer","family":"Rana","sequence":"additional","affiliation":[{"name":"Cardiff University, UK"}]},{"given":"Charith","family":"Perera","sequence":"additional","affiliation":[{"name":"Cardiff University, UK"}]}],"member":"320","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Intersoft Consulting. 2020. 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Bandara , Muhammad Ajmal , Blaine Price , and Bashar Nuseibeh . 2020. Designing privacy-aware Internet of Things applications. Info. Sci. 512 (Mar . 2020 ), 238\u2013257. Retrieved from http:\/\/arxiv.org\/abs\/1703.03892. Charith Perera, Mahmoud Barhamgi, Arosha K. Bandara, Muhammad Ajmal, Blaine Price, and Bashar Nuseibeh. 2020. Designing privacy-aware Internet of Things applications. Info. Sci. 512 (Mar. 2020), 238\u2013257. Retrieved from http:\/\/arxiv.org\/abs\/1703.03892."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2991561.2991566"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.042313.00197"},{"key":"e_1_2_1_32_1","unstructured":"Privacypatterns.eu. 2016. Collecting patterns for better privacy. Retrieved from https:\/\/privacypatterns.eu.  Privacypatterns.eu. 2016. Collecting patterns for better privacy. Retrieved from https:\/\/privacypatterns.eu."},{"key":"e_1_2_1_33_1","unstructured":"Privacypatterns.org. 2015. 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Rubinstein and Nathaniel Good . 2013 . Privacy by design: A counterfactual analysis of Google and Facebook privacy incidents . Berkeley Technol. Law J. 28 , 2 (2013), 1333 \u2013 1413 . DOI:https:\/\/doi.org\/10.2139\/ssrn.2128146 arxiv:arXiv:1011.1669v3 10.2139\/ssrn.2128146 Ira S. Rubinstein and Nathaniel Good. 2013. Privacy by design: A counterfactual analysis of Google and Facebook privacy incidents. Berkeley Technol. Law J. 28, 2 (2013), 1333\u20131413. DOI:https:\/\/doi.org\/10.2139\/ssrn.2128146 arxiv:arXiv:1011.1669v3","journal-title":"Berkeley Technol. 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