Crafting a balance between big data utility and protection in the semantic data cloud
Yuh-Jong Hu, Kua-Ping Cheng, Ya-Ling Huang · 2013
Structured big data of Personal Identifiable Information (PII) are acquired from everywhere and stored as microdata in a statistical database. Given a statistical disclosure control method, big data analysis and protection are enacted for outsourcing data sources. We flexibly glean the data utility to achieve effective data-driven decision-making. However, we still comply with the privacy protection principles while applying data analysis. In this paper, we propose three types of semantics-enabled policies for controlling access, handling data, and releasing data to craft a balance between data utility and protection. Structured big data are tagged with semantic metadata to enable semantics-enabled policy's direct processing and interpretation. Finally, we demonstrate how to craft a balance between data utility and protection with these types of semantics-enabled policies, combined with various statistical disclosure control methods.