IRIS : Multi-Party Computation

IRIS：多方计算

• 批准号: EP/M016803/1
• 负责人: Nigel Smart
• 金额: $ 8.98万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM016803%2F1
• 关键词: IRIS; Multi; Party; Computation

Secure two-party and multiparty computation enables a set of parties with private inputs to securely compute a joint function of their inputs. The security properties guaranteed are privacy (nothing but the output is revealed), correctness (the correct output is obtained, even if some parties behave maliciously), and independence of inputs (no party can make its input depend on other parties' inputs). In the case where a majority of the participating parties can be assumed to be honest, then it is also possible to guarantee fairness (meaning that if one party obtains output then all parties obtain output). These security properties must hold in the face of adversarial behaviour. The main adversary models that have been considered are semi-honest adversaries (who follow the protocol specification but try to learn more than allowed from the communication transcript), and malicious adversaries (who can follow any arbitrary strategy in an attempt to break security).Despite the stringent requirements on protocols for secure computation, it was already shown in the mid 1980s that any efficient function can be securely computed in the presence of malicious adversaries. This powerful feasibility result has enormous implications! Any distributed task between machines which may or may not be honest (either because the party owning the machine is dishonest, or the machine has been hacked into and taken over by an adversary) can be carried out securely, without any concern that private information will be stolen, or that an incorrect result may be obtained. Thus, secure computation can be used to solve a vast number of problems thatoccur in the modern digital era. These problems include auctions, elections, digital cash, anonymous transactions, private database queries, cloud security, privacy-preserving data mining, private statistics aggregation, private biometric verification, and more. This potential and broad applicability has made secure computation a heavily-studied field over the last 25 years. The Bristol group have conducted ground breaking research over the last six years to turn MPC into a practical reality. This work has been taken up by a number of groups world wide. This proposal aims to extend this work and forge long standing research links with colleagues in India

安全的两方和多方计算使一组具有私有输入的各方能够安全地计算其输入的联合函数。保证的安全属性是隐私（只显示输出），正确性（即使某些方的行为是恶意的，也会获得正确的输出）和输入的独立性（任何一方都不能使其输入依赖于其他方的输入）。在大多数参与方可以被假设为诚实的情况下，也可以保证公平性（这意味着如果一方获得输出，则所有各方都获得输出）。这些安全属性必须在对抗行为面前保持不变。已经考虑的主要对手模型是半诚实的对手（他们遵循协议规范，但试图从通信记录中了解更多信息），以及恶意对手（他们可以遵循任何任意策略来试图破坏安全性）。尽管对安全计算的协议有严格的要求，在20世纪80年代中期已经表明，在存在恶意对手的情况下，可以安全地计算任何有效函数。这个强大的可行性结果具有巨大的意义！机器之间的任何分布式任务都可以安全地执行，而不必担心私人信息会被窃取，或者可能会获得错误的结果。因此，安全计算可以用来解决现代数字时代出现的大量问题。这些问题包括拍卖、选举、数字现金、匿名交易、私有数据库查询、云安全、隐私保护数据挖掘、私有统计数据聚合、私有生物特征验证等。这种潜在的和广泛的适用性使得安全计算在过去的25年里成为一个被广泛研究的领域。布里斯托集团在过去六年中进行了开创性的研究，将MPC变为现实。这项工作已由世界各地的一些团体进行。该提案旨在扩展这项工作，并与印度的同事建立长期的研究联系