Research Article
Balancing Activity Recognition and Privacy Preservation with a Multi-objective Evolutionary Algorithm
@INPROCEEDINGS{10.1007/978-3-030-91421-9_1, author={Angelica Poli and Angela M. Mu\`{o}oz-Ant\^{o}n and Susanna Spinsante and Francisco Florez-Revuelta}, title={Balancing Activity Recognition and Privacy Preservation with a Multi-objective Evolutionary Algorithm}, proceedings={Smart Objects and Technologies for Social Good. 7th EAI International Conference, GOODTECHS 2021, Virtual Event, September 15--17, 2021, Proceedings}, proceedings_a={GOODTECHS}, year={2022}, month={1}, keywords={Human Activity Recognition Privacy preservation Gender user identification Wearable sensors Multi-objective evolutionary algorithm}, doi={10.1007/978-3-030-91421-9_1} }- Angelica Poli
Angela M. Muñoz-Antón
Susanna Spinsante
Francisco Florez-Revuelta
Year: 2022
Balancing Activity Recognition and Privacy Preservation with a Multi-objective Evolutionary Algorithm
GOODTECHS
Springer
DOI: 10.1007/978-3-030-91421-9_1
Abstract
With the widespread of miniaturized inertial sensors embedded in wearable devices, an increasing number of individuals monitor their daily life activities through consumer electronic products. However, long-lasting data collection (e.g., from accelerometer) may expose the users to privacy violations, such as the leakage of personal details. To help mitigate these aspects, we propose an approach to conceal subject’s personal attributes (i.e., gender) while maximizing the accuracy on both the monitoring and recognition of human activity. In particular, a Multi-Objective Evolutionary Algorithm (MOEA), namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is applied to properly weight input features extracted from the raw accelerometer data acquired with a wrist-worn device (Empatica E4). Experiments were conducted on a large-scale and real life dataset, and validated by adopting the Random Forest algorithm with 10-fold cross validation. Findings demonstrate that the proposed method can highly limit gender recognition (from 89.37% using all the features to 64.38% after applying the MOEA algorithm) while only reducing the accuracy of activity recognition by 5.45% points (from 89.59% to 84.14%).
