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PCPR: Plaintext Compression and Plaintext Reconstruction for Reducing Memory Consumption on Homomorphically Encrypted CNN

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Abstract

In the big data era, data privacy is a concern for everybody. Adopting homomorphic encryption is a promising way of preserving data privacy; however, it consumes large memory. Previously proposed lazy encoding encapsulates a vector into one data on demand, decreasing memory consumption. However, it results in 2.10–2.49× application latency increase in our experiment, compared to without lazy encoding. This paper proposes a novel technique called plaintext compression and plaintext reconstruction (PCPR), a lightweight pre-encoding and on-demand processing, which achieves almost the same memory consumption decrease as lazy encoding with a shorter latency. Our ideas are 1) dividing data into masks and corresponding scalars and 2) using lightweight operations instead of encoding. Experimental results show that PCPR achieves 1.16–2.03× shorter latency with 0.07–0.15 GiB larger memory consumption than lazy encoding, reducing memory consumption by 16.97–68.17 GiB compared to a method without lazy encoding and PCPR.

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Notes

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    Parameters of ConsineLRScheduler are as follows: t_initial is 1,000, lr_min and warmup_lr_init are\({10}^{-4}\), warmup_t is 100, and warmup_prefix is true.

  4. 4.

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Authors and Affiliations

  1. Waseda University, Tokyo, Japan

    Takuya Suzuki & Hayato Yamana

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  1. Takuya Suzuki

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  2. Hayato Yamana

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Correspondence toTakuya Suzuki.

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Suzuki, T., Yamana, H. (2024). PCPR: Plaintext Compression and Plaintext Reconstruction for Reducing Memory Consumption on Homomorphically Encrypted CNN. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-031-57916-5_11

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