Abstract
Federated machine learning is a promising paradigm allowing organizations to collaborate toward the training of a joint model without the need to explicitly share sensitive or business-critical datasets. Previous works demonstrated that such paradigm is not sufficient to preserve confidentiality of the training data, even to honest participants. In this work, we extend a well-known framework for training sparse Support Vector Machines in a distributed setting, while preserving data confidentiality by means of a novel non-interactive secure multiparty computation engine, that preserves data confidentiality. We formally demonstrate the security properties of the engine and provide, by means of extensive empirical evaluation, the performance of the extended framework both in terms of accuracy and execution time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ajmera, A.: Framingham heart study dataset (2018). Data Accessed from Kaggle. https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset
Brisimi, T.S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I.C., Shi, W.: Federated learning of predictive models from federated electronic health records. Int. J. Med. Inform. 112, 59–67 (2018)
Catalano, D., Gennaro, R., Howgrave-Graham, N.: The bit security of Paillier’s encryption scheme and its applications. In: Pfitzmann, B. (ed.) EUROCRYPT 2001. LNCS, vol. 2045, pp. 229–243. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44987-6_15
Chen, H., Dai, W., Kim, M., Song, Y.: Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. Cryptology ePrint Archive, Report 2019/524 (2019). https://eprint.iacr.org/2019/524
CSIRO Data61: Python Paillier Library (2013). https://github.com/data61/python-paillier
Dawber, T.R., Meadors, G.F., Moore, F.E.: Epidemiological approaches to heart disease: the Framingham Study. Am. J. Public Health Nations Health 41(3), 279–286 (1951)
Detrano, R., et al.: International application of a new probability algorithm for the diagnosis of coronary artery disease. Am. J. Cardiol. 64(5), 304–310 (1989)
Du, W., Han, Y.S., Chen, S.: Privacy-preserving multivariate statistical analysis: linear regression and classification. In: Berry, M.W., Dayal, U., Kamath, C., Skillicorn, D.B. (eds.) Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA, 22–24 April 2004, pp. 222–233. SIAM (2004)
Erdös, P., Rényi, A.: On random graphs I. Publicationes Math. Debrecen 6, 290 (1959)
Fang, H., Qian, Q.: Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet 13(4), 94 (2021)
Flury, B.: The multivariate normal distribution. In: Flury, B. (ed.) A First Course in Multivariate Statistics. STS, pp. 171–207. Springer, New York (1997). https://doi.org/10.1007/978-1-4757-2765-4_3
Guo, J., Liu, Z., Lam, K., Zhao, J., Chen, Y., Xing, C.: Secure weighted aggregation in federated learning. CoRR abs/2010.08730 (2020)
Kantarci, B., Labatut, V.: Classification of complex networks based on topological properties. In: 2013 International Conference on Cloud and Green Computing, Karlsruhe, Germany, 30 September–2 October 2013, pp. 297–304. IEEE Computer Society (2013)
Ludwig, H., et al.: IBM federated learning: an enterprise framework. White Paper V0.1 (2020)
Lyu, L., et al.: Privacy and robustness in federated learning: attacks and defenses. CoRR abs/2012.06337 (2020)
Ma, J., Naas, S.A., Sigg, S., Lyu, X.: Privacy-preserving federated learning based on multi-key homomorphic encryption (2021)
Mammen, P.M.: Federated learning: opportunities and challenges. CoRR abs/2101.05428 (2021)
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data (2017)
Melis, L., Song, C., Cristofaro, E.D., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE Symposium on Security and Privacy, SP 2019, San Francisco, CA, USA, 19–23 May 2019, pp. 691–706. IEEE (2019)
Newman, M.E.J.: Random graphs as models of networks, pp. 35–68. Wiley (2002)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16
Patel, J., Tejalupadhyay, S., Patel, S.: Heart disease prediction using machine learning and data mining technique, March 2016
Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: Cavallaro, L., et al. (eds.) Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec@CCS 2019, London, UK, 15 November 2019, pp. 1–11. ACM (2019)
Xu, R., Baracaldo, N., Zhou, Y., Anwar, A., Ludwig, H.: Hybridalpha: an efficient approach for privacy-preserving federated learning. In: Cavallaro, L., et al. (eds.) Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec@CCS 2019, London, UK, 15 November 2019, pp. 13–23. ACM (2019)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 12:1–12:19 (2019)
Yin, L., Feng, J., Xun, H., Sun, Z., Cheng, X.: A privacy-preserving federated learning for multiparty data sharing in social IoTs. IEEE Trans. Netw. Sci. Eng. 8(3), 2706–2718 (2021)
Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: BatchCrypt: efficient homomorphic encryption for cross-silo federated learning. In: Gavrilovska, A., Zadok, E. (eds.) 2020 USENIX Annual Technical Conference, USENIX ATC 2020, 15–17 July 2020, pp. 493–506. USENIX Association (2020)
Acknowledgement
Stefano Braghin and Theodora Brisimi are partially funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 824988. https://musketeer.eu/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bottoni, S., Braghin, S., Brisimi, T., Trombetta, A. (2021). Privacy-Preserving Distributed Support Vector Machines. In: Rezig, E.K., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2021 2021. Lecture Notes in Computer Science(), vol 12921. Springer, Cham. https://doi.org/10.1007/978-3-030-93663-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-93663-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93662-4
Online ISBN: 978-3-030-93663-1
eBook Packages: Computer ScienceComputer Science (R0)