Abstract
Jointing multi-source data for model training can improve the accuracy of neural network. To solve the raising privacy concerns caused by data sharing, data are generally encrypted and outsourced to a group of cloud servers for computing and processing. In this client-cloud architecture, we propose FPPNet, a fast and privacy-preserving neural network for secure inference on sensitive data. FPPNet is deployed in three cloud servers, who collaboratively execute privacy computing via three-party arithmetic secret sharing. We develop the secure conversion method between additive shares and multiplicative shares, and propose three secure protocols to calculate non-linear functions, such as comparison, exponent and division that are superior to prior three-party works. Some secure modules for running convolutional, ReLU, max-pooling and Sigmoid layers are designed to implement FPPNet. We theoretically analyze the security and complexity of the proposed protocols. With MNIST dataset and two types of neural networks, experimental results validate that our FPPNet is faster than the related works, and the accuracy is the same as that of plaintext neural network.
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References
Xiong, J., Bi, R., Chen, Q., et al.: Towards edge-collaborative, lightweight and secure region proposal network. J. Commun. 41(10), 188–201 (2020)
Xiong, J., Bi, R., Zhao, M., et al.: Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles. IEEE Wirel. Commun. 27(3), 24–30 (2020)
Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: IEEE Symposium on Security and Privacy (SP), USA, pp. 19–38 (2017)
Bi, R., Chen, Q., Xiong, J., et al.: Design method of secure computing protocol for deep neural network. Chin. J. Netw. Inf. Secur. 6(4), 130–139 (2020)
Xiong, J., Zhou, Y., Bi, R., et al.: Towards edge-collaborative, lightweight and privacy-preserving classification framework. J. Commun. 43(1), 127–137 (2022)
Wagh, S., Tople, S., Benhamouda, F., et al.: FALCON: honest-majority maliciously secure framework for private deep learning. Proc. Priv. Enhancing Technol. 1, 188–208 (2021)
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
Mohassel, P., Rindal, P.: ABY3: a mixed protocol framework for machine learning, In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), Los Angeles, USA, pp. 35–52 (2018)
Wagh, S., Gupta, D., Chandran, N.: SecureNN: 3-party secure computation for neural network training. Proc. Priv. Enhancing Technol. 2019(3), 26–49 (2019)
Demmler, D., Schneider, T., Zohner, M.: ABY-A framework for efficient mixed-protocol secure two-party computation. In: Proceedings of the Network and Distributed System Security Symposium (NDSS), San Diego, USA, pp. 1–15 (2015)
Rouhani, B.D., Riazi, M.S., Koushanfar, F.: DeepSecure: scalable provably-secure deep learning. In: Proceedings of the 55th Annual Design Automation Conference (DAC), San Francisco, USA, pp. 1–6 (2018)
Liu, J., Juuti, M., Lu, Y., et al.: Oblivious neural network predictions via MiniONN transformations. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), Los Angeles, USA, pp. 619–631 (2017)
Riazi, M.S., Weinert, C., Tkachenko, O., et al.: Chameleon: a hybrid secure computation framework for machine learning applications. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security (ASIACCS), New York, USA, pp. 707–721 (2018)
Juvekar, C., Vaikuntanathan, V., Chandrakasan, A.: GAZELLE: a low latency framework for secure neural network inference. In: 27th USENIX Security Symposium (USENIX Security), Berkeley, USA, pp. 1651–1669 (2018)
Mishra, P., Lehmkuhl, R., Srinivasan, A., et al.: DELPHI: a cryptographic inference service for neural networks. In: 29th USENIX Security Symposium (USENIX Security), Boston, USA, pp. 2505–2522 (2020)
Huang, K., Liu, X., Fu, S., et al.: A lightweight privacy-preserving CNN feature extraction framework for mobile sensing. IEEE Trans. Dependable Secure Comput. 18(3), 1441–1455 (2021)
Chaudhari, H., Choudhury, A., Patra, A., et al.: ASTRA: high throughput 3PC over rings with application to secure prediction. In: Proceedings of the ACM SIGSAC Conference on Cloud Computing Security Workshop (CCSW), Los Angeles, USA, pp. 81–92 (2019)
Patra, A., Suresh, A.: BLAZE: blazing fast privacy-preserving machine learning. In: 27th Annual Network and Distributed System Security Symposium (NDSS), San Diego, USA, pp. 1–18 (2020)
Byali, M., Chaudhari, H., Patra, A., et al.: FLASH: fast and robust framework for privacy-preserving machine learning. Proc. Priv. Enhancing Technol. 2, 459–480 (2020)
Chaudhari, H., Rachuri, R., Suresh, A.: Trident: efficient 4PC framework for privacy preserving machine learning. In: Proceedings of the Network and Distributed System Security Symposium (NDSS), San Diego, USA, pp. 1–18 (2020)
Xiong, J., Bi, R., Tian, Y., et al.: Towards lightweight, privacy-preserving cooperative object classification for connected autonomous vehicles. IEEE Internet Things J. 9(4), 2787–2801 (2022)
Markstein, P.: The new IEEE-754 standard for floating point arithmetic. In: Dagstuhl Seminar Proceedings, pp. 1–3 (2008)
Damgård, I., Fitzi, M., Kiltz, E., et al.: Unconditionally secure constant-rounds multi-party computation for equality, comparison, bits and exponentiation. In: Theory of Cryptography Conference (TCC), New York, USA, pp. 285–304 (2006)
Bogdanov, D., Laur, S., Willemson, J.: Sharemind: a framework for fast privacy-preserving computations. In: Jajodia, S., Lopez, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192–206. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88313-5_13
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61872088, Grant U1905211, Grant 62072109, and Grant U1804263; in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS202105; in part by the Science and Technology Major Support Program of Guizhou Province under Grant 20183001; in part by the Science and Technology Program of Guizhou Province under Grant 20191098; in part by the Project of High-level Innovative Talents of Guizhou Province under Grant 20206008; and in part by the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province under Grant ZCL21015.
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Bi, R., Xiong, J., Li, Q., Liu, X., Tian, Y. (2022). FPPNet: Fast Privacy-Preserving Neural Network via Three-Party Arithmetic Secret Sharing. In: Chenggang, Y., Honggang, W., Yun, L. (eds) Mobile Multimedia Communications. MobiMedia 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-031-23902-1_13
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