Abstract
Along with the widespread use of smartphones, activity recognition using embedded inertial sensors has intrigued researchers. The learning and employing activity recognition systems suffers from some challenges. Training data of different persons should be involved in the learning procedure. In contrast, the activities of each individual should remain private. In addition, the style of different individuals activities makes the training phase become a challenging problem. This paper presents two privacy-preserving transfer learning algorithms for activity recognition application namely as \(P^{2}S^{2}{} { TM}\) and \(P^{2}{} { DS}^{2}{} { TM}\). The \(P^{2}S^{2}{} { TM}\) algorithm is designed based on preserving data privacy by the client, while the \(P^{2}{} { DS}^{2}{} { TM}\) algorithm trusts the preservation of the data privacy by both the client and the server. The performance of the proposed algorithms is influenced by a random matrix. The performances of the algorithms are compared with those of the classic PPSVM classifier for different dimensions of the random matrix. The evaluation of the proposed algorithms revealed superior performance comparing with PPSVM in different scenarios. For the random matrix with the dimension of \(500\times 561\), The \(P^{2}S^{2}{} { TM}\) and \(P^{2}{} { DS}^{2}{} { TM}\) algorithms are able to relatively reduce privacy-preserving activity recognition error rate by 68% and 16% respectively; while preserving privacy with an acceptable rate.
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Bisio, I., Delfino, A., Lavagetto, F., & Sciarrone, A. (2016). Enabling IoT for in-home rehabilitation: Accelerometer signals classification methods for activity and movement recognition. IEEE Internet of Things Journal, 4662, 1–1.
Cao, L., Wang, Y., Zhang, B., Jin, Q., & Vasilakos, A. V. (2017). GCHAR: An efficient group-based context aware human activity recognition on smartphone. Journal of Parallel and Distributed Computing, 118, 67–80.
Rokni, S. A., & Ghasemzadeh, H. (2018). Autonomous training of activity recognition algorithms in mobile sensors: A transfer learning approach in context-invariant views. IEEE Transactions on Mobile Computing, 1233, 1–14.
Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2015). A survey of online activity recognition using mobile phones. Sensors, 15(1), 2059–2085.
Morales, J., & Akopian, D. (2017). Physical activity recognition by smartphones, a survey. Biocybernetics and Biomedical Engineering, 37(3), 388–400.
Gong, Y., Fang, Y., Guo, Y., Member, S., Fang, Y., & Guo, Y. (2016). Private data analytics on biomedical sensing data via distributed computation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(3), 431–444.
Sahi, M. A., et al. (2018). Privacy preservation in e-Healthcare environments: State of the art and future directions. IEEE Access, 6, 464–478.
Mangasarian, O. L., Wild, E. W., & Fung, G. M. (2007). Privacy-preserving classification of horizontally partitioned data via random kernels. Technical report, 07-03, data min. Institute, Department of Computer Science, University of Wisconsin-Madison.
Qiang, J., Yang, B., Li, Q., & Jing, L. (2011). Privacy-preserving SVM of horizontally partitioned data for linear classification. In: Proceedings of the 4th International Congress on Image Signal Process, CISP 2011, (vol. 5, pp. 2771–2775).
Yu, H., Jiang, X., & Vaidya, J. (2006). Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In: Proceedings of the 2006 ACM symposium on applied computing (vol. 3918, pp. 603–610).
Hu, Y., He, G., Fang, L., & Tang, J. (2010). Privacy-preserving SVM classification on arbitrarily partitioned data. In:Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010 (vol. 1, pp. 67–71).
Sun, L., Mu, W.-S., Qi, B., & Zhou, Z.-J. (2015). A new privacy-preserving proximal support vector machine for classification of vertically partitioned data. International Journal of Machine Learning and Cybernetics, 6(1), 109–118.
Mangasarian, O. (2010). Privacy-preserving random kernel classification of checkerboard partitioned data. Data Mining, 8, 375–387.
Zhu, H., Liu, X., Lu, R., & Li, H. (2017). Efficient and privacy-preserving online medical prediagnosis framework using nonlinear SVM. IEEE Journal Of Biomedical And Health Informatics, 21(3), 838–850.
Gheid, Z., & Challal, Y. (2016). Novel efficient and privacy-preserving protocols for sensor-based human activity recognition. In Intl IEEE conferences.
Wu, W., Parampalli, U., Liu, J., & Xian, M. (2018). Privacy preserving k-nearest neighbor classification over encrypted database in outsourced cloud environments. World Wide Web, 21, 1–23.
Samet, S. (2015). Privacy-preserving logistic regression. Journal of Advances in Information Technology, 6(3), 88–95.
Vaidya, J., KantarcIolu, M., & Clifton, C. (2008). Privacy-preserving Nave Bayes classification. The VLDB Journal, 17(4), 879–898.
Huai, M., Huang, L., Yang, W., Li, L., & Qi, M. (2015). Privacy-preserving Naive Bayes classification. In: International conference on knowledge science, engineering and management (pp. 627–638).
Li, T., Li, J., Liu, Z., Li, P., & Jia, C. (2018). Differentially private Naive Bayes learning over multiple data sources. Information Sciences, 444, 89–104.
Gao, C., Cheng, Q., He, P., Susilo, W., & Li, J. (2018). Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Information Sciences, 444, 72–88.
Cook, D., Feuz, K. D., & Krishnan, N. C. (2013). Transfer learning for activity recognition: A survey. Knowledge and information systems, 36(3), 537–556.
Abdallah, Z. S., Gaber, M. M., Srinivasan, B., & Krishnaswamy, S. (2012). StreamAR: Incremental and active learning with evolving sensory data for activity recognition. In: IEEE 24th international conference on tools with artificial intelligence (pp. 1163–1170).
Garcia-Ceja, E., & Brena, R. (2015). Building personalized activity recognition models with scarce labeled data based on class similarities. In: International conference on ubiquitous computing and ambient intelligence (pp. 265–276).
Fallahzadeh, R., & Ghasemzadeh, H. (2017). Personalization without user interruption: Boosting activity recognition in new subjects using unlabeled data. In ICCPS ’17 Proceedings of the 8th international conference on cyber-physical systems (pp. 293–302).
Zhang, X. Y., & Liu, C. L. (2013). Writer adaptation with style transfer mapping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1773–1787.
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. New York: Cambridge University Press.
Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013). A public domain dataset for human activity recognition using smartphones. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 24–26).
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Hashemian, M., Razzazi, F., Zarrabi, H. et al. A privacy-preserving distributed transfer learning in activity recognition. Telecommun Syst 72, 69–79 (2019). https://doi.org/10.1007/s11235-018-0534-1
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DOI: https://doi.org/10.1007/s11235-018-0534-1