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2024 | OriginalPaper | Buchkapitel

Privacy-Preserving Tree-Based Inference with TFHE

verfasst von : Jordan Frery, Andrei Stoian, Roman Bredehoft, Luis Montero, Celia Kherfallah, Benoit Chevallier-Mames, Arthur Meyre

Erschienen in: Mobile, Secure, and Programmable Networking

Verlag: Springer Nature Switzerland

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Abstract

Fully Homomorphic Encryption is a powerful tool for processing encrypted data and is particularly adapted to the type of programs that are common in machine learning (ML). On tabular data, tree-based ML models obtain state-of-the-art results, are more robust, and are easier to use and deploy than neural networks. We introduce an implementation of privacy-preserving decision tree evaluation based on the TFHE scheme, leveraging optimized representations for encrypted integer and TFHE’s powerful programmable bootstrapping mechanism. Our technique is applicable to decision trees, random forests, and gradient boosted trees. We demonstrate our approach on popular datasets and show that accuracy on encrypted data is very close the one obtained by the same models applied to clear data, while latency is competitive with the state of the art.

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Akavia, A., Leibovich, M., Resheff, Y.S., Ron, R., Shahar, M., Vald, M.: Privacy-preserving decision trees training and prediction. ACM Transactions on Privacy and Security 25(3), 1–30 (2022)CrossRef

Al-Rubaie, M., Chang, J.M.: Privacy-preserving machine learning: threats and solutions. IEEE Secur. Priv. 17(2), 49–58 (2019)CrossRef

Bergerat, L., et al.: Parameter optimization & larger precision for (T)FHE. Cryptology ePrint Archive, Paper 2022/704 (2022). https://eprint.iacr.org/2022/704

Boldyreva, A., Chenette, N., Lee, Y., O’Neill, A.: Order-preserving symmetric encryption. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 224–241. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01001-9_13CrossRef

Boldyreva, A., Chenette, N., O’Neill, A.: Order-preserving encryption revisited: improved security analysis and alternative solutions. In: Rogaway, P. (ed.) CRYPTO 2011. LNCS, vol. 6841, pp. 578–595. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22792-9_33CrossRef

Bonawitz, K., et al.: Towards federated learning at scale: system design. Proc. Mach. Learn. Syst. 1, 374–388 (2019)

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Routledge, London (2017)CrossRef

Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. In ITCS 2012, 309–325 (2012)MathSciNetCrossRef

Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. ACM Trans. Comput. Theory 6(3), 13:1–13:36 (2014)

Bourse, F., Minelli, M., Minihold, M., Paillier, P.: Fast homomorphic evaluation of deep discretized neural networks. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018. LNCS, vol. 10993, pp. 483–512. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96878-0_17CrossRef

Brakerski, Z.: Fully homomorphic encryption without modulus switching from classical GapSVP. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 868–886. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_50CrossRef

Banerjee, M., Reynolds, E., Andersson, H.B., Nallamothu, B.K.: Tree-based analysis: a practical approach to create clinical decision-making tools. Circ.: Cardiov. Qual. Outcomes 12(5), e004879 (2019)

Cong, K., Das, D., Park, J., Pereira, H.V.: SortingHat: efficient private decision tree evaluation via homomorphic encryption and transciphering. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS 2022, pp. 563–577, New York, NY, USA. Association for Computing Machinery (2022)

Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Faster fully homomorphic encryption: bootstrapping in less than 0.1 seconds. In: Cheon, J.H., Takagi, T. (eds.) ASIACRYPT 2016. LNCS, vol. 10031, pp. 3–33. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53887-6_1CrossRef

Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 377–408. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_14CrossRef

Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: TFHE: fast fully homomorphic encryption over the torus. J. Cryptol. 33(1), 34–91 (2020)MathSciNetCrossRef

Chillotti, I., Joye, M., Paillier, P.: Programmable bootstrapping enables efficient homomorphic inference of deep neural networks. In: Dolev, S., Margalit, O., Pinkas, B., Schwarzmann, A. (eds.) CSCML 2021. LNCS, vol. 12716, pp. 1–19. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78086-9_1CrossRef

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_15CrossRef

Ducas, L., Micciancio, D.: FHEW: bootstrapping homomorphic encryption in less than a second. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015. LNCS, vol. 9056, pp. 617–640. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46800-5_24CrossRef

Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1CrossRef

Fan, J., Vercauteren, F.: Somewhat practical fully homomorphic encryption. Cryptology ePrint Archive, Report 2012/144 (2012). https://ia.cr/2012/144

Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)

Gentry, C.: A fully homomorphic encryption scheme. PhD thesis, Stanford University (2009). crypto.stanford.edu/craig

Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, Bethesda, MD, USA, 31 May–2 June 2009, pp. 169–178 (2009)

Goldreich, O.: Secure multi-party computation. Manuscript. Preliminary Version 78(110) (1998)

Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? In: Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)

Gentry, C., Sahai, A., Waters, B.: Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-based. In: Canetti, R., Garay, J.A. (eds.) CRYPTO 2013. LNCS, vol. 8042, pp. 75–92. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40041-4_5CrossRef

Huynh, D.: Cryptotree: fast and accurate predictions on encrypted structured data. CoRR, abs/2006.08299 (2020)

Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704–2713 (2018)

Joye, M.: Guide to fully homomorphic encryption over the [discretized] torus. Cryptology ePrint Archive, Paper 2021/1402 (2021). https://eprint.iacr.org/2021/1402

Meyre, A., et al.: Concrete-ML: a privacy-preserving machine learning library using fully homomorphic encryption for data scientists (2022). https://github.com/zama-ai/concrete-ml

Meng, X., Feigenbaum, J.: Privacy-preserving xgboost inference. arXiv preprint arXiv:2011.04789 (2020)

Microsoft: Hummingbird library (2022). https://github.com/microsoft/hummingbird

Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Berlin (1999). https://doi.org/10.1007/3-540-48910-x_16CrossRef

Rivest, R.L., Shamir, A., Adleman, L.M.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)MathSciNetCrossRef

Shwartz-Ziv, R., Armon, A.: Tabular data: deep learning is not all you need. Inf. Fusion 81, 84–90 (2022)CrossRef

Tueno, A., Boev, Y., Kerschbaum, F.: Non-interactive private decision tree evaluation. In: Singhal, A., Vaidya, J. (eds.) DBSec 2020. LNCS, vol. 12122, pp. 174–194. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49669-2_10CrossRef

Zama: Announcing concrete numpy. Zama Blog (2022). https://www.zama.ai/post/announcing-concrete-numpy

Titel: Privacy-Preserving Tree-Based Inference with TFHE
verfasst von: Jordan Frery
Andrei Stoian
Roman Bredehoft
Luis Montero
Celia Kherfallah
Benoit Chevallier-Mames
Arthur Meyre
Verlag: Springer Nature Switzerland
Buch: Mobile, Secure, and Programmable Networking
Print ISBN: 978-3-031-52425-7

Electronic ISBN: 978-3-031-52426-4

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-52426-4_10

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