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https://doi.org/10.1007/978-3-...
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A Practical TFHE-Based Multi-Key Homomorphic Encryption with Linear Complexity and Low Noise Growth

Authors: Akın, Yavuz; Klemsa, Jakub; Önen, Melek;

A Practical TFHE-Based Multi-Key Homomorphic Encryption with Linear Complexity and Low Noise Growth

Abstract

Fully Homomorphic Encryption enables arbitrary computations over encrypted data and it has a multitude of applications, e.g., secure cloud computing in healthcare or finance. Multi-Key Homomorphic Encryption (MKHE) further allows to process encrypted data from multiple sources: the data can be encrypted with keys owned by different parties. In this paper, we propose a new variant of MKHE instantiated with the TFHE scheme. Compared to previous attempts by Chen et al. and by Kwak et al., our scheme achieves computation runtime that is linear in the number of involved parties and it outperforms the faster scheme by a factor of 4.5-6.9×, at the cost of a slightly extended pre-computation. In addition, for our scheme, we propose and practically evaluate parameters for up to 128 parties, which enjoy the same estimated security as parameters suggested for the previous schemes (100 bits). It is also worth noting that our scheme-unlike the previous schemes-did not experience any error in any of our nine experiments, each running 1 000 trials.

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Keywords

Multi-key homomorphic encryptionTFHE schemeSecure cloud computing, Secure cloud computing, TFHE scheme, Multi-key homomorphic encryption, [INFO] Computer Science [cs], [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
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