Data sharing, key to effective research, raises an interesting challenge in the digital era: how does one present data in a way that preserves the privacy or anonymity of the data, yet allows sufficient access to information needed for research? This tradeoff—between accessibility and over-exposure—has numerous implications, notably in such critical sectors as energy and healthcare, where research needs may conflict with privacy concerns.
I3P researchers are exploring this tradeoff with the aid of a model that relates analytical capabilities to privacy parameters. Drawing on a set of ontologies that show real-world inference, such as the link between diagnosis and gender in medical records, the model includes sanitizing agents that adapt over time to changing data.
Funded by the Department of Homeland Security (DHS)
