Improved Building Blocks for Secure Multi-Party Computation based on Secret Sharing with Honest Majority
Improved Building Blocks for Secure Multi-Party Computation based on Secret Sharing with Honest Majority
复制标题
基于诚实多数秘密共享的安全多方计算的改进构建模块
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Chen Yuan
中科院分区：
文献类型：
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作者：
Marina Blanton;Ah Reum Kang;Chen Yuan
安全多方计算允许在不向参与者披露数据的情况下对私有数据进行任何所需功能的评估。由于用户、客户或患者数据的收集日益增加，以及在不披露数据的情况下分析分布在不同组织中的数据集的需求，它越来越受欢迎。因为安全计算技术的采用取决于它们在实践中的性能，所以持续提高其性能是很重要的。在这项工作中，我们专注于许多类型的程序所使用的常见非平凡操作，其中它们性能的任何进步都会影响依赖它们的程序的运行时间。特别是，我们处理在私有位置读取或写入数组元素以及整数乘法的操作。这项工作的重点是在半诚实安全模型中具有诚实多数的秘密共享设置。我们通过分析和实证评估证明了所提出的技术相对于先前构造的改进。
Secure multi-party computation permits evaluation of any desired functionality on private data without disclosing the data to the participants. It is gaining its popularity due to increasing collection of user, customer, or patient data and the need to analyze data sets distributed across different organizations without disclosing them. Because adoption of secure computation techniques depends on their performance in practice, it is important to continue improving their performance. In this work, we focus on common non-trivial operations used by many types of programs, where any advances in their performance would impact the runtime of programs that rely on them. In particular, we treat the operation of reading or writing an element of an array at a private location and integer multiplication. The focus of this work is on secret sharing setting with honest majority in the semi-honest security model. We demonstrate improvement of the proposed techniques over prior constructions via analytical and empirical evaluation.