A Study on Quantized Parameters for Protection of a Model and Its Inference Input
Author:
Affiliation:
1. Osaka University
Publisher
Information Processing Society of Japan
Subject
General Computer Science
Link
https://www.jstage.jst.go.jp/article/ipsjjip/31/0/31_667/_pdf
Reference60 articles.
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3. [3] Dalskov, A.P.K., Escudero, D. and Keller, M.: Secure Evaluation of Quantized Neural Networks, Proc. Privacy Enhancing Technologies, Vol.2020, No.4, pp.355-375 (2020).
4. [4] Jiang, X., Kim, M., Lauter, K.E. and Song, Y.: Secure Outsourced Matrix Computation and Application to Neural Networks, Proc. CCS 2018, pp.1209-1222, ACM (2018).
5. [5] Kitai, H., Cruz, J.P., Yanai, N., Nishida, N., Oba, T., Unagami, Y., Teruya, T., Attrapadung, N., Matsuda, T. and Hanaoka, G.: MOBIUS: Model-Oblivious Binarized Neural Networks, IEEE Access, Vol.7, pp.139021-139034 (2019).
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