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Differentially private human activity recognition for smartphone users

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Abstract

User privacy is an important concern that should be handled in data intensive applications. Interestingly, differential privacy is a privacy model that can be applied to such datasets. This model is advantageous as it does not make any strong assumption about the adversary. In this work, we have introduced the notion of differential privacy in the domain of Human Activity Recognition (HAR). Real life accelerometer data has been collected from different smartphone configurations that were carried by the users in different manner according to their convenience. Our contribution in this work is to propose a privacy preserving HAR framework incorporating algorithms to preserve the differential privacy of the user data. The algorithm exploits the scalar and the vector parts of the accelerometer readings and applies privacy preserving mechanisms on it. A Deep Multi Layer Perceptron (DMLP) framework has been utilized for activity classification. We have achieved comparatively similar results with an enhanced surplus of achievement of privacy in terms of data and are so far the first of its kind in the aforementioned domain of HAR based on smartphone sensing data. The proposed framework is implemented both on collected real life dataset capturing different smartphone configurations and usage behavior and benchmark datasets.

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Notes

  1. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/

  2. https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones

  3. https://www.cis.fordham.edu/wisdm/dataset.php

  4. https://www.stepsetgo.com/

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Authors and Affiliations

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

    Avishek Garain, Rudrajit Dawn, Saswat Singh & Chandreyee Chowdhury

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  1. Avishek Garain
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  2. Rudrajit Dawn
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  3. Saswat Singh
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  4. Chandreyee Chowdhury
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Correspondence to Chandreyee Chowdhury.

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Garain, A., Dawn, R., Singh, S. et al. Differentially private human activity recognition for smartphone users. Multimed Tools Appl 81, 40827–40848 (2022). https://doi.org/10.1007/s11042-022-13185-4

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  • DOI: https://doi.org/10.1007/s11042-022-13185-4

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