TY - GEN
T1 - Privacy Preserving Human Activity Recognition Using Microaggregated Generative Deep Learning
AU - Aleroud, Ahmed
AU - Shariah, Majd
AU - Malkawi, Rami
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Internet of Things (IoT) allow data collection and sharing of many aspects of our daily life. With the huge amount of data transferred via IoT devices, privacy concerns need to be addressed while preserving the usefulness of the shared data. This research proposes a microaggregation-generative privacy preserving model for human activity recognition in IoT data. While generative deep neural networks have been used for data perturbation, data leakage and disclosing private information of the training sample is still an issue when applying the traditional deep learning generative models. We proposed a novel approach to perturb IoT data using Generative Adversarial Networks and Microaggregation while preserving the utility of its features. Our approach reduces the size of the original dataset by employing an entropy-preserving measure to discard noisy records after anonymization. The performance of the proposed approach was measured using several criteria such as classification accuracy, precision, recall, and F-score before and after anonymization. As a result, the proposed GAN-Microaggregation privacy preserving technique showed a remarkable performance in term of accuracy. Moreover, the privacy was measured, showing the benefits of the proposed approach to share IoT datasets with minimal privacy attacking surface.
AB - Internet of Things (IoT) allow data collection and sharing of many aspects of our daily life. With the huge amount of data transferred via IoT devices, privacy concerns need to be addressed while preserving the usefulness of the shared data. This research proposes a microaggregation-generative privacy preserving model for human activity recognition in IoT data. While generative deep neural networks have been used for data perturbation, data leakage and disclosing private information of the training sample is still an issue when applying the traditional deep learning generative models. We proposed a novel approach to perturb IoT data using Generative Adversarial Networks and Microaggregation while preserving the utility of its features. Our approach reduces the size of the original dataset by employing an entropy-preserving measure to discard noisy records after anonymization. The performance of the proposed approach was measured using several criteria such as classification accuracy, precision, recall, and F-score before and after anonymization. As a result, the proposed GAN-Microaggregation privacy preserving technique showed a remarkable performance in term of accuracy. Moreover, the privacy was measured, showing the benefits of the proposed approach to share IoT datasets with minimal privacy attacking surface.
KW - Generative Adversarial Networks (GAN)
KW - Internet of Things (IoT)
KW - data reduction
KW - de-identification
KW - human activity recognition
KW - privacy-preserving data analysis
UR - http://www.scopus.com/inward/record.url?scp=85137325243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137325243&partnerID=8YFLogxK
U2 - 10.1109/CSR54599.2022.9850328
DO - 10.1109/CSR54599.2022.9850328
M3 - Conference contribution
AN - SCOPUS:85137325243
T3 - Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022
SP - 357
EP - 363
BT - Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022
Y2 - 27 July 2022 through 29 July 2022
ER -