IoT-Detective: Analyzing IoT data under differential privacy

The success of emerging IoT applications depends on integrating privacy protections into the IoT infrastructures to guard against privacy risks posed by sensor-based continuous monitoring of individuals and their activities. This demonstration adapts a recentlyproposed system, PeGaSus [2], which releases streaming data under the formal guarantee of differential privacy, with a state-of-the-art IoT testbed (TIPPERS [9]) located at UC Irvine. PeGaSus protects individuals' data by introducing distortion into the output stream. While PeGaSuS has been shown to offer lower numerical error compared to competing methods, assessing the usefulness of the output is application dependent. The goal of the demonstration is to assess the usefulness of private streaming data in a real-world IoT application setting. The demo consists of a game, IoT-Detective, in which participants carry out visual data analysis tasks on private data streams, earning points when they achieve results similar to those on the true data stream. The demo will educate participants about the impact of privacy mechanisms on IoT data while at the same time generate insights into privacy-utility trade-offs in IoT applications.

Duke Scholars

Author Ashwinkumar Venkatanaga Machanavajjhala Computer Science