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.
ISBN - Is published in 9781538643877 (urn:isbn:9781538643877)
Start page
348
End page
359
Total pages
12
Outlet
Proceedings of the 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 2018)