Abstract
The growing popularity of cloud-based deep learning raises a problem about accurate prediction and data privacy. Previous studies have implemented privacy prediction for simple neural networks. Since more complex neural networks require more computational overhead, existing privacy prediction schemes are inefficient. To tackles the above problem, this paper introduces a privacy prediction method for lightweight convolutional neural network (CNN) that can be applied to encrypted data. Firstly, the complex CNN is pruned into a lightweight network without compromising the original accuracy, which can realize secure prediction efficiently. Secondly, the FV homomorphic encryption scheme is adopted to encrypt the user’s sensitive data and each layer in CNN is calculated on the ciphertext, so as to protect user’s data privacy. Finally, the security analysis and experiment results demonstrate the privacy-preserving property and practicability of the proposed scheme, where the complex CNN on the MNIST data set can achieve more than 98% accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Angelini, E., di Tollo, G., Roli, A.: A neural network approach for credit risk evaluation. Q. Rev. Econ. Finance 48(4), 733–755 (2008)
Blakley, G.R.: Safeguarding cryptographic keys. In: 1979 International Workshop on Managing Requirements Knowledge (MARK), pp. 313–318. IEEE (1979)
Bradley, R.E., D’Antonio, L.A., Sandifer, C.E.: Euler at 300. Mathematical Association of America, Washington DC (2007)
Chopra, S., Yadav, D., Chopra, A.: Artificial neural networks based Indian stock market price prediction: before and after demonetization. J. Swarm Intell. Evol. Comput. 8(174), 2 (2019)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Fan, J., Vercauteren, F.: Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive 2012/144 (2012)
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, pp. 169–178 (2009)
Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201–210 (2016)
Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game, or a completeness theorem for protocols with honest majority. In: Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio Micali, pp. 307–328. ACM (2019)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) IWANN 1995. LNCS, vol. 930, pp. 195–201. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59497-3_175
Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1959)
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)
Liu, J., Juuti, M., Lu, Y., Asokan, N.: Oblivious neural network predictions via minionn transformations. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 619–631 (2017)
Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 19–38. IEEE (2017)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)
Pei, D., Salomaa, A., Ding, C.: Chinese Remainder Theorem: Applications in Computing, Coding. Cryptography. World Scientific, Singapore (1996)
Riazi, M.S., Weinert, C., Tkachenko, O., Songhori, E.M., Schneider, T., Koushanfar, F.: Chameleon: a hybrid secure computation framework for machine learning applications. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security, pp. 707–721 (2018)
Rouhani, B.D., Riazi, M.S., Koushanfar, F.: DeepSecure: scalable provably-secure deep learning. In: Proceedings of the 55th Annual Design Automation Conference, pp. 1–6 (2018)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)
Smart, N.P., Vercauteren, F.: Fully homomorphic SIMD operations. Des. Codes Crypt. 71(1), 57–81 (2014). https://doi.org/10.1007/s10623-012-9720-4
Wagh, S., Gupta, D., Chandran, N.: SecureNN: 3-party secure computation for neural network training. Proc. Priv. Enhanc. Technol. 2019(3), 26–49 (2019)
Yao, A.C.: Protocols for secure computations. In: 23rd Annual Symposium on Foundations of Computer Science (SFCS 1982), pp. 160–164. IEEE (1982)
Acknowledgment
This work is supported by Science and Technology Project of Guangzhou city (No. 201707010320).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, S., Li, Y., Zhou, D., Wei, L., Gan, Q. (2020). Privacy Prediction of Lightweight Convolutional Neural Network. In: Xu, G., Liang, K., Su, C. (eds) Frontiers in Cyber Security. FCS 2020. Communications in Computer and Information Science, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9739-8_39
Download citation
DOI: https://doi.org/10.1007/978-981-15-9739-8_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9738-1
Online ISBN: 978-981-15-9739-8
eBook Packages: Computer ScienceComputer Science (R0)