Xiao Wang
Patrick Tague
ABSTRACT
A large-scale sensing infrastructure can collect ample data to benefit many real-world applications. One promising application scenario is building management. However, exposure of the sensor data potentially reveals private details about building users. In this paper, we investigate indoor location privacy as a motivating example to manifest potential privacy risks in smart buildings. We apply inference techniques to reconstruct users' location traces from room-level occupancy data. Unlike other types of surveillance that are dedicated to explicit tracking such as security cam- eras, time-series occupancy traces, as aggregated environmental measurements, are typically deemed privacy-preserving. Unfortunately, it may still reveal some of the same sensitive information as privacy-invasive sensing such as video surveillance. We con- duct experiments using a publicly available dataset and synthetic data. Our results demonstrate the underlying privacy leakage via occupancy data. We further show how our evaluation can enable adaptive privacy mechanisms to control the information leakage by the sensing system.
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Index Terms
- Non-Invasive User Tracking via Passive Sensing: Privacy Risks of Time-Series Occupancy Measurement
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