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Privacy-Preserving Spatio-Temporal Patient Data Publishing
31st International Conference on Database and Expert Systems Applications (DEXA) ; 12392:407-416, 2020.
Article in English | Web of Science | ID: covidwho-1530236
ABSTRACT
As more data become available to the public, the value of information seems to be diminishing with concern over what constitute privacy of individual. Despite benefit to data publishing, preserving privacy of individuals remains a major concern because linking of data from heterogeneous source become easier due to the vast availability of artificial intelligence tools. In this paper, we focus on preserving privacy of spatio-temporal data publishing. Specifically, we present a framework consisting of (i) a 5-level temporal hierarchy to protect the temporal attributes and (ii) temporal representative point (TRP) differential privacy to protect the spatial attributes. Evaluation results on big datasets show that our framework keeps a good balance of utility and privacy. To a further extent, our solution is expected be extendable for privacypreserving data publishing for the spatio-temporal data of coronavirus disease 2019 (COVID-19) patients.

Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: 31st International Conference on Database and Expert Systems Applications (DEXA) Year: 2020 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: 31st International Conference on Database and Expert Systems Applications (DEXA) Year: 2020 Document Type: Article