Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Sinnott, Richard O. and Verspoor, Karin (2018) Semantic-Based Policy Composition for Privacy-Demanding Data Linkage. In: 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE

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Abstract

Record linkage can be used to support current and future health research across populations however such approaches give rise to many challenges related to patient privacy and confidentiality including inference attacks. To address this, we present a semantic-based policy framework where linkage privacy detects attribute associations that can lead to inference disclosure issues. To illustrate the effectiveness of the approach, we present a case study exploring health data combining spatial, ethnicity and language information from several major on-going projects occurring across Australia. Compared with classic access control models, the results show that our proposal outperforms other approaches with regards to effectiveness, reliability and subsequent data utility.

Item Type:	Book Section
Status:	Published
DOI:	https://doi.org/10.1109/TrustCom/BigDataSE.2018.00060
Subjects:	Q Science > Q Science (General) > Q325 Machine learning Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software Q Science > QA Mathematics > QA76.9.H85 Human-Computer Interaction; Virtual Reality; Mixed Reality; Augmented Reality ; Extended Reality R Medicine > RA Public aspects of medicine R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
School/Department:	School of Science, Technology and Health
URI:	https://ray.yorksj.ac.uk/id/eprint/5257

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