论文标题

基于添加剂秘密共享的有效保留隐私计算

Efficient Privacy-Preserving Computation Based on Additive Secret Sharing

论文作者

Xiong, Lizhi, Zhou, Wenhao, Xia, Zhihua, Gu, Qi, Weng, Jian

论文摘要

云计算的出现为用户提供了一个新的计算范式 - 可以将大规模且复杂的计算任务外包给云服务器。但是,也随之而来的是隐私问题。完全同态加密在保护隐私计算方面具有巨大的潜力,但尚未准备好进行实践。目前,安全的多方计算(MPC)仍然主要是处理敏感数据的方法。在本文中,根据基于秘密共享的MPC范式,我们提出了一种安全的2方计算方案,其中云服务器可以以高效率安全地评估功能。我们首先根据典型的添加剂秘密共享(ASS)提出乘法秘密共享(MSS)。然后,我们设计协议以在MSS和ASS之间切换共享的秘密,基于一系列用于比较的协议,并提出了几乎所有基本功能。我们证明,所有提出的协议在诚实但充满幽默的模型中都具有普遍性的安全性。最后,我们将在通信效率和功能完整性上展示协议的显着进度。

The emergence of cloud computing provides a new computing paradigm for users -- massive and complex computing tasks can be outsourced to cloud servers. However, the privacy issues also follow. Fully homomorphic encryption shows great potential in privacy-preserving computation, yet it is not ready for practice. At present, secure multiparty computation (MPC) remains mainly approach to deal with sensitive data. In this paper, following the secret sharing based MPC paradigm, we propose a secure 2-party computation scheme, in which cloud servers can securely evaluate functions with high efficiency. We first propose the multiplicative secret sharing (MSS) based on typical additive secret sharing (ASS). Then, we design protocols to switch shared secret between MSS and ASS, based on which a series of protocols for comparison and nearly all of the elementary functions are proposed. We prove that all the proposed protocols are Universally Composable secure in the honest-but-curious model. Finally, we will show the remarkable progress of our protocols on both communication efficiency and functionality completeness.

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