IoT-Detective: Analyzing IoT Data Under Differential Privacy
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Ghayyur, Sameera, Yan Chen, Roberto Yus, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau, and Sharad Mehrotra. “IoT-Detective: Analyzing IoT Data Under Differential Privacy.” In Proceedings of the 2018 International Conference on Management of Data, 1725–28. SIGMOD ’18. New York, NY, USA: Association for Computing Machinery, 2018. https://doi.org/10.1145/3183713.3193571.
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Abstract
Emerging IoT technologies promise to bring revolutionary changes to many domains including health, transportation, and building management. However, continuous monitoring of individuals threatens privacy. The success of IoT thus depends on integrating privacy protections into IoT infrastructures. This demonstration adapts a recently-proposed system, PeGaSus, which releases streaming data under the formal guarantee of differential privacy, with a state-of-the-art IoT testbed (TIPPERS) 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 generating insights into privacy-utility trade-offs in IoT applications.