Franz Papst
ABSTRACT
Machine learning has a lot of potential when applied to time series sensor data, yet a lot of this potential is currently not utilized, due to privacy concerns of parties in charge of this data. In this work I want to apply privacy-preserving techniques to machine learning for time series data, in order to unleash the dormant potential of this type of data.
- P. Brockwell and R. Davis. 2016. Introduction to Time Series and Forecasting (third ed.). Springer International Publishing.Google Scholar
- C. Dwork. 2006. Differential Privacy. In Automata, Languages and Programming. Springer.Google ScholarDigital Library
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27. 2672--2680.Google Scholar
- F. Grandl, J. Kofler, M. Suntinger, P. Majcen, M. Mayerhofer, F. Papst, O. Saukh, M. Fallast, A. Turkaspa, F. Steininger, K. Linke, J. Duda, T. Wittek, F.J. Auer, and C. Egger-Danner. 2019. Opportunities and Challenges of New Technologies for Performance Recording with Focus on Claw Health and Metabolism. In New Traits and Adding Value to the Recording and ID Services in the Animal Production. Proceedings of the 43rd ICAR Conference (ICAR Technical Series), Vol. 24.Google Scholar
- O. Gupta and R. Raskar. 2018. Distributed Learning of Deep Neural Network over Multiple Agents. Journal of Network and Computer Applications 116 (2018).Google Scholar
- D. Kingma and M. Welling. 2014. Auto-Encoding Variational Bayes. arXiv:1312.6114 (2014).Google Scholar
- C. Mayer. 2009. Security and Privacy Challenges in the Internet of Things. Electronic Communications of the EASST 17 (2009).Google Scholar
- H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B.A. Arcas. 2016. Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv:1602.05629 [cs] (2016). arXiv:1602.05629Google Scholar
- A. Narayanan and V. Shmatikov. 2008. Robust De-Anonymization of Large Sparse Datasets. In Proceedings of the IEEE 2008 IEEE Symposium on Security and Privacy (S&P 2008). 111--125.Google ScholarDigital Library
- F. Papst, O. Saukh, K. Römer, F. Grandl, I. Jakovljevic, F. Steininger, M. Mayerhofer, J. Duda, and C. Egger-Danner. 2019. Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints. In Proceedings of the 9th International Conference on the Internet of Things (IoT '19). ACM.Google Scholar
- F. Papst, N. Stricker, A. Oberegger, and O. Saukh. 2020. On Location Privacy in Shared Sensor Data. To be submitted.Google Scholar
- F. Papst, N. Stricker, and O. Saukh. 2020. Poster Abstract: Localization from Activity Sensor Data. In Proceedings of the 18th ACM Conference on Embedded Networked Sensor Systems (SenSys '20). ACM.Google Scholar
- L. Sweeney. 1997. Weaving Technology and Policy Together to Maintain Confidentiality. The Journal of Law, Medicine & Ethics 25, 2--3 (1997), 98--110.Google Scholar
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Privacy-preserving machine learning for time series data: PhD forum abstract
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