G. D. Fulk and E. Sazonov, Using sensors to measure activity in people with stroke, Topics in Stroke Rehabilitation, vol.18, p.746757, 2011.

E. Park, H. Chang, and H. S. Nam, Use of machine learning classiers and sensor data to detect neurological decit in stroke patients, Journal of Medical Internet Research, vol.19, issue.4, p.120, 2017.

J. Qi, P. Yang, D. Fan, and Z. Deng, A survey of physical activity monitoring and assessment using internet of things technology, International Conference on Computer and Information Technology, pp.2353-2358, 2015.

J. Han, E. Owusu, L. T. Nguyen, A. Perrig, and J. Zhang, Accomplice: Location inference using accelerometers on smartphones, International Conference on Communication Systems and Networks, p.19, 2012.

J. L. Kröger, P. Raschke, and T. R. Bhuiyan, Privacy implications of accelerometer data: a review of possible inferences, International Conference on Cryptography, Security and Privacy, p.8187, 2019.

C. Benabdelkader, R. Cutler, and L. Davis, Stride and cadence as a biometric in automatic person identication and verication, International Conference on Automatic Face Gesture Recognition, p.372377, 2002.

H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. Muller, Deep learning for time series classication: a review, Data Mining and Knowledge Discovery, vol.33, issue.4, p.917963, 2019.

M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, Certifying and removing disparate impact, International Conference on Knowledge Discovery and Data Mining, p.259268, 2015.

F. Schäfer and A. Anandkumar, Competitive gradient descent, Advances in Neural Information Processing Systems, p.76237633, 2019.

J. L. Reyes-ortiz, Smartphone-based human activity recognition, 2015.

G. Vavoulas, C. Chatzaki, T. Malliotakis, M. Pediaditis, and M. Tsiknakis, The mobiact dataset: Recognition of activities of daily living using smartphones, International Conference on Information and Communication Technologies for Ageing Well, p.143151, 2016.

T. Jourdan, A. Boutet, and C. Frindel, Toward privacy in iot mobile devices for activity recognition, International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, p.155165, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01882330

M. Malekzadeh, R. G. Clegg, A. Cavallaro, and H. Haddadi, Protecting sensory data against sensitive inferences, Workshop on Privacy by Design in Distributed Systems, p.6, 2018.

N. Raval, A. Machanavajjhala, and J. Pan, Olympus: Sensor privacy through utility aware obfuscation, Privacy Enhancing Technologies, vol.2019, issue.1, 2019.

M. Malekzadeh, R. G. Clegg, A. Cavallaro, and H. Haddadi, Mobile sensor data anonymization, Internet of Things Design and Implementation, 2019.

A. Abadleh, E. Al-hawari, E. Alkafaween, and H. Al-sawalqah, Step detection algorithm for accurate distance estimation using dynamic step length, International Conference on Mobile Data Management, p.324327, 2017.

H. F. Nweke, Y. W. Teh, M. A. Al-garadi, and U. R. Alo, Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges, Expert Systems with Applications, vol.105, p.233261, 2018.

S. , R. Ramamurthy, and N. Roy, Recent trends in machine learning for human activity recognition -a survey, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.8, issue.4, p.1254, 2018.

C. Liu, S. Chakraborty, and P. Mittal, Deeprotect: Enabling inference-based access control on mobile sensing applications, 2017.

M. Alzantot, S. Chakraborty, and M. Srivastava, Sensegen: A deep learning architecture for synthetic sensor data generation, International Conference on Pervasive Computing and Communications Workshops, p.188193, 2017.

J. Chen, J. Konrad, and P. Ishwar, Vgan-based image representation learning for privacypreserving facial expression recognition, 2018.

H. Edwards and A. Storkey, Censoring representations with an adversary, 2015.

W. Oleszkiewicz, P. Kairouz, K. Piczak, R. Rajagopal, and T. Trzcinski, Siamese generative adversarial privatizer for biometric data, 2018.

A. Tripathy, Y. Wang, and P. Ishwar, Privacy-preserving adversarial networks, Annual Allerton Conference on Communication, Control, and Computing, p.495505, 2019.

M. Romanelli, C. Palamidessi, and K. Chatzikokolakis, Generating optimal privacyprotection mechanisms via machine learning, 2019.

N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park et al., Data synthesis based on generative adversarial networks, International Conference on Very Large Data Bases, vol.11, p.10711083, 2018.

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, RR n°9325 RESEARCH CENTRE GRENOBLE -RHÔNE-ALPES Inovallée 655 avenue de l'Europe Montbonnot 38334 Saint Ismier Cedex Publisher Inria Domaine de Voluceau -Rocquencourt BP 105 -78153 Le Chesnay Cedex inria, pp.249-6399