Optimizing MPC for Robust and Scalable Integer and Floating-Point Arithmetic

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• 关键词：Secure Multiparty Computation ; Floating ; point operations ; Protocol design
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9604
• 期：1
• 页码：271-287
• 全文大小：251 KB
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• 作者单位：Liisi Kerik (19)
  Peeter Laud (19)
  Jaak Randmets (19) (20)
  
  19. Cybernetica AS, Tartu, Estonia
  20. University of Tartu, Tartu, Estonia
  
• 丛书名：Financial Cryptography and Data Security
• ISBN：978-3-662-53357-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9604

文摘

Secure multiparty computation (SMC) is a rapidly maturing field, but its number of practical applications so far has been small. Most existing applications have been run on small data volumes with the exception of a recent study processing tens of millions of education and tax records. For practical usability, SMC frameworks must be able to work with large collections of data and perform reliably under such conditions. In this work we demonstrate that with the help of our recently developed tools and some optimizations, the Sharemind secure computation framework is capable of executing tens of millions integer operations or hundreds of thousands floating-point operations per second. We also demonstrate robustness in handling a billion integer inputs and a million floating-point inputs in parallel. Such capabilities are absolutely necessary for real world deployments.