Abstract
Machine learning servers with mass storage and computing power is an ideal platform to store, manage, and analyze data and support decision-making. However, the main issue is providing security and privacy to the data, as the data is stored in a public way. Recently, homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. In this paper, we proposed an encrypted all convolutional net that transformed traditional all convolutional net into a net based on homomorphic encryption. This scheme allows different data holders to send their encrypted data to cloud service, complete predictions, and return them in encrypted form as the cloud service provider does not have a secret key. Therefore, the cloud service provider and others cannot get unencrypted raw data. When applied to the MNIST database, privacy-preserving all convolutional based on homomorphic encryption predict efficiently, accurately and with privacy protection.
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Acknowledgements
This work is supported by National Cryptography Development Fund of China under grant number MMJJ20170112, National Natural Science Foundation of China (Grant Nos. U1636114, 61772550, 61572521), National Key Research and Development Program of China (Grant No. 2017YFB0802000), Natural Science Basic Research Plan in Shaanxi Province of China (Grant Nos. 2018JM6028, 2016JQ6037).
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Liu, W., Pan, F., Wang, X.A., Cao, Y., Tang, D. (2019). Privacy-Preserving All Convolutional Net Based on Homomorphic Encryption. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_66
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DOI: https://doi.org/10.1007/978-3-319-98530-5_66
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