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Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

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Abstract

In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users’ identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

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Acknowledgements

The research work of Asif Iqbal Middya is funded by “NET-JRF (National Eligibility Test-Junior Research Fellowship) scheme of the University Grants Commission, Government of India”. This research work is also supported by the project entitled Participatory and Realtime Pollution Monitoring System For Smart City, funded by Higher Education, Science & Technology and Biotechnology, Department of Science & Technology, Government of West Bengal, India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Asif Iqbal Middya & Sarbani Roy

  2. Department of Electrical Engineering, Jadavpur University, Kolkata, India

    Saptarshi Mandal & Rahul Talukdar

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  1. Asif Iqbal Middya
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Correspondence to Sarbani Roy.

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Middya, A.I., Roy, S., Mandal, S. et al. Privacy protected user identification using deep learning for smartphone-based participatory sensing applications. Neural Comput & Applic 33, 17303–17313 (2021). https://doi.org/10.1007/s00521-021-06319-6

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