Abstract
Logistic regression, as one of the classification method, is widely used in machine learning. Due to the complexity of training process, outsourcing the training task to a third party is a feasible choice, while the plain or direct outsourcing will inevitibaly lead to privacy leakage. To address this problem, this paper proposes an efficient privacy-preserving outsourced logistic regression (EPoLORE) scheme. In securely training the model, we design related protocols: floating-point conversion, integer multiplication, vector inner product, and activation function based on a distributed double-trap public key cryptosystem (DT-PKC), allowing the cloud server to effectively perform the integer and floating-point computations with ciphertexts of training data. In such a way, the privacy of training data is preserved and the model can obtain the accuracy approximate to that of the regular model trained in plaintext. The Security of the protocols is analyzed, thereby demonstrating that EPoLORE meets the security requirements. The corresponding experiments show the effectiveness of the proposed scheme and the comparison of model accuracy with the regular training model.
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References
Jain, D., Singh, V.: A two-phase hybrid approach using feature selection and adaptive SVM for chronic disease classification. Int. J. Comput. Appl. 43, 524–536 (2021)
Dumitrescu, E., Hué, S., Hurlin, C., Tokpavi, S.: Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects. Eur. J. Oper. Res. 297, 1178–1192 (2022)
Szafraniec-Siluta, E., Zawadzka, D., Strzelecka, A.: Application of the logistic regression model to assess the likelihood of making tangible investments by agricultural enterprises. Procedia Comput. Sci. 207, 3894–3903 (2022)
Han, K., Hong, S., Cheon, J.H., Park, D.: Logistic regression on homomorphic encrypted data at scale. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 9pp. 466–9471 (2019)
Mandal, K., & Gong, G.: PrivFL: practical privacy-preserving federated regressions on high-dimensional data over mobile networks. In: Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, pp. 57–68 (2019)
Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy, pp. 19–38 (2017)
Cheon, J.H., Kim, D., Kim, Y., Song, Y.: Ensemble method for privacy-preserving logistic regression based on homomorphic encryption. IEEE Access 6, 46938–46948 (2018)
Shi, H., et al.: Secure multi-pArty computation grid LOgistic REgression (SMAC-GLORE). BMC Med. Inform. Decis. Mak. 16, 175–187 (2016)
Li, J., et al.: Efficient and secure outsourcing of differentially private data publishing with multiple evaluators. IEEE Trans. Dependable Secure Comput. 19(1), 67–76 (2020)
Zhu, L., Tang, X., Shen, M., Gao, F., Zhang, J., Du, X.: Privacy-preserving machine learning training in IoT aggregation scenarios. IEEE Internet Things J. 8(15), 12106–12118 (2021)
Aono, Y., Hayashi, T., Trieu Phong, L., Wang, L.: Scalable and secure logistic regression via homomorphic encryption. In: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pp. 142–144 (2016)
Carpov, S., Gama, N., Georgieva, M., Troncoso-Pastoriza, J.R.: Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption. Cryptology ePrint Archive (2019)
Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 409–437. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_15
Liu, X., Deng, R.H., Choo, K.K.R., Weng, J.: An efficient privacy-preserving outsourced calculation toolkit with multiple keys. IEEE Trans. Inf. Forensics Secur. 11, 2401–2414 (2016)
Sebbouh, O., Cuturi, M., Peyré, G.: Randomized stochastic gradient descent ascent. In: International Conference on Artificial Intelligence and Statistics, pp. 2941–2969 (2022)
Goldreich, O.: Foundations of Cryptography: Volume 2, Basic Applications. Cambridge University Press, Cambridge (2009)
Wang, J., Wu, L., Wang, H., Choo, K.K.R., He, D.: An efficient and privacy-preserving outsourced support vector machine training for internet of medical things. IEEE Internet Things J. 8(1), 458–473 (2020)
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Zhang, W., Sun, Y., Yan, S., Wang, H., Liu, Y., Zhang, C. (2024). EPoLORE: Efficient and Privacy Preserved Logistic Regression Scheme. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14510. Springer, Singapore. https://doi.org/10.1007/978-981-99-9788-6_6
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DOI: https://doi.org/10.1007/978-981-99-9788-6_6
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