Abstract
The SVM classifier has been a significant and prevailing technique in machine learning applications. Training a high-quality SVM classifier usually requires a huge amount of data, which makes collaborative training by multiple parties become an inevitable trend. However, it causes privacy risks when sharing sensitive data with others. There are some existing methods to solve this problem. These methods mainly contain computation-intensive cryptographic techniques which are inefficient and not suitable for practical use. Therefore, it is important to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving SVM classifier training scheme based on blockchain. We establish a blockchain-based SVM classifier training mechanism which realizes collaboratively training while protecting privacy. We adopt the additive secret sharing technique to design several computation protocols, which are much more efficient than the schemes which contain complex cryptographic primitives. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.
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Hua, J., et al.: Cinema: efficient and privacy-preserving online medical primary diagnosis with skyline query. IEEE Internet Things J. 6(2), 1450–1461 (2018)
Ma, Z., Liu, Y., Liu, X., Ma, J., Ren, K.: Lightweight privacy-preserving ensemble classification for face recognition. IEEE Internet Things J. 6(3), 5778–5790 (2019). Kindly provide the volume number and page range for Refs. [5 and 15], if applicable
Ghazanfar, M., Prugel-Bennett, A.: An improved switching hybrid recommender system using Naive Bayes classifier and collaborative filtering (2010)
Bennett, K.P., Demiriz, A.: Semi-supervised support vector machines. In: Advances in Neural Information processing systems, pp. 368–374 (1999)
Li, J., et al.: Searchable symmetric encryption with forward search privacy. IEEE Trans. Dependable Secur. Comput. (2019)
Liu, Z., Li, B., Huang, Y., Li, J., Xiang, Y., Pedrycz, W.: NewMCOS: towards a practical multi-cloud oblivious storage scheme. IEEE Trans. Knowl. Data Eng. 32, 714–727 (2019)
Accountability Act: Health insurance portability and accountability act of 1996. Public Law 104, 191 (1996)
Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: NDSS, vol. 4324, p. 4325 (2015)
Zhu, H., Liu, X., Rongxing, L., Li, H.: Efficient and privacy-preserving online medical prediagnosis framework using nonlinear SVM. IEEE J. Biomed. Health Inform. 21(3), 838–850 (2016)
Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)
Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)
Shen, M., Tang, X., Zhu, L., Xiaojiang, D., Guizani, M.: Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities. IEEE Internet Things J. 6(5), 7702–7712 (2019)
Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420–432. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-46766-1_34
Damgård, I., Fitzi, M., Kiltz, E., Nielsen, J.B., Toft, T.: Unconditionally secure constant-rounds multi-party computation for equality, comparison, bits and exponentiation. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 285–304. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_15
Huang, K., Liu, X., Fu, S., Guo, D., Xu, M.: A lightweight privacy-preserving CNN feature extraction framework for mobile sensing. IEEE Trans. Dependable Secure Comput. (2019)
Canetti, R., Cohen, A., Lindell, Y.: A simpler variant of universally composable security for standard multiparty computation. In: Gennaro, R., Robshaw, M. (eds.) CRYPTO 2015. LNCS, vol. 9216, pp. 3–22. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48000-7_1
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
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Jia, N., Fu, S., Xu, M. (2020). Privacy-Preserving Nonlinear SVM Classifier Training Based on Blockchain. In: Xiang, Y., Liu, Z., Li, J. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2020. Communications in Computer and Information Science, vol 1298. Springer, Singapore. https://doi.org/10.1007/978-981-15-9031-3_25
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DOI: https://doi.org/10.1007/978-981-15-9031-3_25
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