Abstract
Decision tree is a favored prediction model in machine learning and data mining. With the fast development and wide application of machine learning, the privacy of decision tree prediction is a rising concern.
In this paper, We construct a specific purpose NIZK for privacy-preserving decision tree prediction. The protocol allows the server who holds a decision tree model to convince others the result of the decision tree on an encrypted data sample, without leaking private information about the decision tree. Our protocol has high efficiency in both prover time and verifier time, and the proof size is only several KBs. With such NIZK, we can build a public verifiable private decision tree prediction system. In this system, a client can query the result of the server’s decision tree on its encrypted feature vector, and anyone who has only the access to public information can verify the validity of the result.
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Acknowledgments
This work was supported by PlatON, the National Natural Science Foundation of China (Grant No. 61932019, No. 61772521 and No. 61772522), Key Research Program of Frontier Sciences, CAS (Grant No. QYZDB-SSW-SYS035), and the Open Project Program of the State Key Laboratory of Cryptology.
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Wang, H., Deng, Y., Xie, X. (2021). Public Verifiable Private Decision Tree Prediction. In: Wu, Y., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2020. Lecture Notes in Computer Science(), vol 12612. Springer, Cham. https://doi.org/10.1007/978-3-030-71852-7_16
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DOI: https://doi.org/10.1007/978-3-030-71852-7_16
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