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SGER: Cryptographic Techniques for Trustworthy Computation in Faulty and Non-Confining Execution Environments

SGER: Cryptographic Techniques for Trustworthy Computation in Faulty and Non-Confining Execution Environments
SGER:在错误和非限制执行环境中进行可信计算的密码技术
批准号:
0808907
负责人:
Ronald Rivest
金额:
$0.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-09-15 至 2010-08-31

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中文摘要
翻译
提案编号:0808907 PI: 罗纳德L Rivest机构: 马萨诸塞州理工学院 SGER:可信计算的密码技术 和无约束执行环境本探索性研究的目的是找到在不安全的、不完美的和可能恶意的环境中进行安全计算的方法。为实现这一目标,正在采取两种相辅相成的办法。 第一个,灵感来自于?证明携带代码,?是a?携带证明的数据?这是一个可以解决当前方法中许多困难的框架。 在这个框架中,系统设计者规定所需的计算属性?的输出,通常表示安全或隐私属性。 这些属性的证明附在流经系统的数据上,并由系统相互验证。的组件。 第二个研究重点是调查的手段,具体实现的计算可以保证安全性能,尽管不可避免的风险。 该方法的新奇在于,风险所基于的假设是可枚举的,因此可以对其进行检查,以确保它们是理想的、最小的、现实的和可验证的。
英文摘要
Proposal Number: 0808907PI: Ronald L RivestInstitution: Massachusetts Institute of TechnologyTitle: SGER: Cryptographic Techniques for Trustworthy Computation in Faulty and Non-Confining Execution EnvironmentsThe objective of this exploratory research is to find methods for conducting secure computation within insecure, imperfect, and possibly malicious environments. To realize this goal, two complementary approaches are being pursued. The first one, inspired by the technique of ?proof carrying code,? is a ?Proof-Carrying Data? framework that can address many of the difficulties with current approaches. In this framework, the system designer prescribe the desired properties of the computation?s output, usually expressing a security or privacy property. Proofs of these properties are attached to the data flowing through the system, and are mutually verified by the system?s components. The second research focus is the investigation of means by which concrete realizations of computation can guarantee security properties despite inevitable risks. The novelty in the method is that the assumptions on which the risks are based are enumeratable, so they can be checked to be sure they that are, ideally, minimal, realistic, and verifiable.
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Efficient Algorithms for Machine Learning
Theoretical Aspects of Machine Learning and Artificial Intelligence
Algorithms, Cryptography and Inference
Concrete Computational Complexity (Computer Research)
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