Abstract
Machine learning enables organizations and individuals to improve efficiency and productivity. With an abundance of data and computational resources, large companies can build complex machine learning models and provide prediction services to clients. One example is decision tree evaluation, where a client can access the trained decision tree model with its input and obtain the classification result. However, the privacy issues on model parameters and clients’ inputs and results need to be addressed. In this paper, we propose a privacy-preserving decision tree evaluation scheme, where we first design an improved interval encoding method that can hide parameters representing an interval. Then, based on the interval encoding method, hash functions, and the Diffie-Hellman key agreement technique, a model owner can generate a set of encodings for the decision tree model and send them to a client, who can determine the classification result based on its input and the encodings. The proposed scheme conceals the model parameters from clients and preserves the data privacy of clients, and only one round of communication between the two entities is needed. We provide a formal security proof that demonstrates the privacy preservation property of our scheme. Performance evaluation shows the practicability of the proposed scheme.
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References
Cao, L.: AI in finance: challenges, techniques, and opportunities. ACM Comput. Surv. (CSUR) 55(3), 1–38 (2022). https://doi.org/10.1145/3502289
Fahle, S., Prinz, C., Kuhlenkötter, B.: Systematic review on machine learning (ML) methods for manufacturing processes-identifying artificial intelligence (AI) methods for field application. Procedia CIRP 93, 413–418 (2020). https://doi.org/10.1016/j.procir.2020.04.109
Liang, J., Qin, Z., Xue, L., Lin, X., Shen, X.: Efficient and privacy-preserving decision tree classification for health monitoring systems. IEEE Internet Things J. 8(16), 12528–12539 (2021). https://doi.org/10.1109/JIOT.2021.3066307
Zhang, Y., Jia, R., Pei, H., Wang, W., Li, B., Song, D.: The secret revealer: generative model-inversion attacks against deep neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 253–261. 10.48550/arXiv. 1911.07135
Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. Cryptology ePrint Archive (2014). 10.14722/ndss.2015.23241
Tai, Raymond K. H.., Ma, Jack P. K.., Zhao, Yongjun, Chow, Sherman S. M..: Privacy-Preserving Decision Trees Evaluation via Linear Functions. In: Foley, Simon N.., Gollmann, Dieter, Snekkenes, Einar (eds.) ESORICS 2017. LNCS, vol. 10493, pp. 494–512. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66399-9_27
Wu, D.J., Feng, T., Naehrig, M., Lauter, K.: Privately evaluating decision trees and random forests. Cryptology ePrint Archive (2015). popets-2016-0043
Banerjee, S., Galbraith, S.D., Russello, G.: Obfuscating decision trees. Cryptology ePrint Archive
Diffie, W., Hellman, M.E.: New directions in cryptography. In: Democratizing Cryptography: The Work of Whitfield Diffie and Martin Hellman, pp. 365–390 (2022). https://doi.org/10.1145/3549993.3550007
Xue, L., Liu, D., Huang, C., Lin, X., Shen, X.S.: Secure and privacy-preserving decision tree classification with lower complexity. J. Commun. Inf. Netw. 5(1), 16–25 (2020)
Liu, L., Chen, R., Liu, X., Su, J., Qiao, L.: Towards practical privacy-preserving decision tree training and evaluation in the cloud. IEEE Trans. Inf. Forensics Secur. 15, 2914–2929 (2020). https://doi.org/10.1109/TIFS.2020.2980192
Damgard, I., Geisler, M., Kroigard, M.: Homomorphic encryption and secure comparison. Int. J. Appl. Crypt. 1(1), 22–31 (2008). https://doi.org/10.1504/IJACT.2008.017048
Hao, Y., Qin, B., Sun, Y.: Privacy-preserving decision-tree evaluation with low complexity for communication. Sensors 23(5), 2624 (2023). https://doi.org/10.3390/s23052624
Barak, B., et al.: On the (IM) possibility of obfuscating programs. J. ACM (JACM) 59(2), 1–48 (2012). https://doi.org/10.1145/2160158.2160159
Boneh, D., Shen, E., Waters, B.: Strongly unforgeable signatures based on computational diffie-hellman. In: Public Key Cryptography-PKC 2006: 9th International Conference on Theory and Practice in Public-Key Cryptography, New York, NY, USA, 24–26 April, 2006. Proceedings 9, pp. 229–240 (2006). https://doi.org/10.1007/11745853_15
Ishai, Y., Kushilevitz, E., Ostrovsky, R.: Sufficient conditions for collision-resistant hashing. In: Theory of Cryptography: Second Theory of Cryptography Conference, TCC 2005, Cambridge, MA, USA, 10–12 February , 2005. Proceedings 2, pp. 445–456 (2005). https://doi.org/10.1007/978-3-540-30576-7_24
MIRACL Library. Website. https://github.com/miracl/MIRACL
Acknowledgement
This project was supported in part by collaborative research funding from the National Research Council of Canada’s Artificial Intelligence for Logistics Program.
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Xue, L., Lin, X., Xiong, P. (2024). Practical and Privacy-Preserving Decision Tree Evaluation with One Round Communication. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_28
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DOI: https://doi.org/10.1007/978-981-99-9785-5_28
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