Abstract
Due to IoT and Industry 4.0, more and more data is collected by sensor nodes, which send their data to a central data lake. This approach results in high data traffic and privacy risk, which we want to address in this paper. Therefore we use an existing Learning from Label Proportions (LLP) algorithm, to use the decentralized properties and extend this approach by applying Differential Privacy to the transferred data. This yields to reduced data transfer and increased privacy.
This research has been funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01IS18038A) and by the German Research Foundation DFG under grant SFB 876 “Providing Information by Resource-Constrained Data Analysis” project B4 “Analysis and Communication for Dynamic Traffic Prognosis”.
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Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Thuraisingham, B.M., Evans, D., Malkin, T., Xu, D. (eds.) Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30–03 November, 2017, pp. 1175–1191. ACM (2017). https://doi.org/10.1145/3133956.3133982
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3-4), 211–407 (2014). https://doi.org/10.1561/0400000042
Fan, H., Liu, Y., Zeng, Z.: Decentralized privacy-preserving data aggregation scheme for smart grid based on blockchain. Sensors 20(18), 5282 (2020). https://doi.org/10.3390/s20185282
Goddard, M.: The EU general data protection regulation (GDPR): European regulation that has a global impact. Int. J. Mark. Res. 59(6), 703–705 (2017)
Grama, M., Musat, M., Muñoz-González, L., Passerat-Palmbach, J., Rueckert, D., Alansary, A.: Robust aggregation for adaptive privacy preserving federated learning in healthcare. CoRR abs/2009.08294 (2020). https://arxiv.org/abs/2009.08294
Groat, M.M., He, W., Forrest, S.: KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks. In: INFOCOM 2011. 30th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 10–15 April 2011, Shanghai, China, pp. 2024–2032. IEEE (2011). https://doi.org/10.1109/INFCOM.2011.5935010
He, W., Liu, X., Nguyen, H., Nahrstedt, K., Abdelzaher, T.F.: PDA: privacy-preserving data aggregation in wireless sensor networks. In: INFOCOM 2007. 26th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 6–12 May 2007, Anchorage, Alaska, USA, pp. 2045–2053. IEEE (2007). https://doi.org/10.1109/INFCOM.2007.237
McCann, B.: A review of scats operation and deployment in Dublin. In: Proceedings of the 19th JCT Traffic Signal Symposium & Exhibition (2014)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Shi, J., Zhang, R., Liu, Y., Zhang, Y.: Prisense: privacy-preserving data aggregation in people-centric urban sensing systems. In: INFOCOM 2010. 29th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 15–19 March 2010, San Diego, CA, USA, pp. 758–766. IEEE (2010). https://doi.org/10.1109/INFCOM.2010.5462147
Stolpe, M., Liebig, T., Morik, K.: Communication-efficient learning of traffic flow in a network of wireless presence sensors. In: Proceedings of the Workshop on Parallel and Distributed Computing for Knowledge Discovery in Data Bases (PDCKDD 2015) (2015)
Stolpe, M., Morik, K.: Learning from label proportions by optimizing cluster model selection. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 349–364. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23808-6_23
Zhang, J., Zhao, Y., Wu, J., Chen, B.: LVPDA: a lightweight and verifiable privacy-preserving data aggregation scheme for edge-enabled IoT. IEEE Internet Things J. 7(5), 4016–4027 (2020). https://doi.org/10.1109/JIOT.2020.2978286
Zhang, W.: Secure data aggregation. In: van Tilborg, H.C.A., Jajodia, S. (eds.) Encyclopedia of Cryptography and Security, 2nd edn., pp. 1104–1105. Springer (2011). https://doi.org/10.1007/978-1-4419-5906-5_639
Zhang, X., Liu, X., Yu, J., Dang, N., Qi, X., Zhang, Q.: Energy-efficient privacy preserving data aggregation protocols based on slicing. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), iThings/GreenCom/CPSCom/SmartData 2019, Atlanta, GA, USA, July 14–17, 2019, pp. 546–551. IEEE (2019). https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00109
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Sachweh, T., Boiar, D., Liebig, T. (2021). Differentially Private Learning from Label Proportions. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham. https://doi.org/10.1007/978-3-030-93736-2_11
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