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Found 5 papers, 5 papers with code
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data
We introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate.
Privacy and Utility Preserving Sensor-Data Transformations
Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications.
Mobile Sensor Data Anonymization
Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions.
Protecting Sensory Data against Sensitive Inferences
Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50\%, the target random guess, from more than 90\% when using raw sensor data.
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis
Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches.
