Search Results for author: Richard G. Clegg

Found 5 papers, 5 papers with code

DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data

2 code implementations5 Aug 2020 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

We introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate.

Activity Recognition Imputation

Privacy and Utility Preserving Sensor-Data Transformations

1 code implementation14 Nov 2019 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

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.

Activity Recognition

Mobile Sensor Data Anonymization

1 code implementation26 Oct 2018 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions.

Activity Recognition

Protecting Sensory Data against Sensitive Inferences

1 code implementation21 Feb 2018 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

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.

Activity Recognition

Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

1 code implementation18 Oct 2017 Mohammad Malekzadeh, Richard G. Clegg, Hamed Haddadi

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.

Activity Recognition Time Series

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