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arXiv 2104.04709cs.CR
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基于秘密共享的实用两方隐私保护神经网络

Practical Two-party Privacy-preserving Neural Network Based on Secret Sharing

Zhengqiang Ge, Zhipeng Zhou, Dong Guo, Qiang Li

摘要:

神经网络具有提供高效预测模型的能力,已被广泛应用于医疗、金融等领域,给我们的生活带来了极大的便利。然而,该模型的高精度需要多方提供大量数据,引发了公众对隐私的担忧。基于多方计算的隐私保护神经网络是当前用来在解决数据隐私的前提下提供模型训练和推理的方法之一。在这项研究中,我们提出了一个新的两方隐私保护神经网络的训练和推理框架中,隐私数据被分发到两个非合谋服务器。我们构造了一个用于掩码生成的预处理协议,在2 PC上支持并实现了秘密共享比较,并提出了一种新的方法来进一步减少通信轮数。在比较协议的基础上,构造了除法、指数等构造块,实现了训练和推理过程不再需要在不同类型的秘密共享之间进行转换,完全基于算术秘密共享。与以往的工作相比,我们的工作获得了更高的准确性,这是非常接近的明文训练。虽然准确性得到了提高,但运行时间却减少了,考虑到在线阶段,我们的工作比SecureML快5倍,比SecureNN快4.32- 5.75倍,并且非常接近当前最佳的3 PC实现,JSPCON。对于安全推理,就已知知识而言,我们应该是目前最优的2 PC实现,比其他作品快4- 358倍。

英文摘要:

Neural networks, with the capability to provide efficient predictive models, have been widely used in medical, financial, and other fields, bringing great convenience to our lives. However, the high accuracy of the model requires a large amount of data from multiple parties, raising public concerns about privacy. Privacy-preserving neural network based on multi-party computation is one of the current methods used to provide model training and inference under the premise of solving data privacy. In this study, we propose a new two-party privacy-preserving neural network training and inference framework in which privacy data is distributed to two non-colluding servers. We construct a preprocessing protocol for mask generation, support and realize secret sharing comparison on 2PC, and propose a new method to further reduce the communication rounds. Based on the comparison protocol, we construct building blocks such as division and exponential, and realize the process of training and inference that no longer needs to convert between different types of secret sharings and is entirely based on arithmetic secret sharing. Compared with the previous works, our work obtains higher accuracy, which is very close to that of plaintext training. While the accuracy has been improved, the runtime is reduced, considering the online phase, our work is 5x faster than SecureML, 4.32-5.75x faster than SecureNN, and is very close to the current optimal 3PC implementation, FALCON. For secure inference, as far as known knowledge is concerned, we should be the current optimal 2PC implementation, which is 4-358x faster than other works.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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