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SecOBig: Security-Preserving Operations on Big Data

SecOBig: Security-Preserving Operations on Big Data
SecOBig:大数据的安全保护操作
批准号:
255319481
负责人:
Professor Dr. Marc Fischlin
金额:
$0.0万
依托单位国家:
德国
项目类别:
Priority Programmes
财政年份:
2014
资助国家:
德国
项目状态:
已结题
起止时间:
2013-12-31 至 2018-12-31

项目摘要

项目成果

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中文摘要
翻译
自从爱德华·斯诺登披露美国国家安全局和其他机构大量滥用大量数据以来,很明显,每个关心其数据隐私的个人和公司都必须采取措施保护数据。对于云存储和云计算场景中的外包数据,以及通过第三方(例如通过Amazon的Elastic MapReduce)处理大数据时,情况更是如此。诸如加密的标准密码手段通常在这里不起作用,因为加密的本质是对所有合理信息进行加扰,数据的语义被隐藏并且不能被第三方用来执行操作;为操作解密数据的选项将违反保护数据不受服务提供商访问的想法。因此,我们需要与期望的操作兼容的密码学。2009年,IBM的研究人员宣布了密码学的突破性成果,他们能够构建允许此类操作的完全同态加密方案。然而,对于全同态加密对大数据集的适用性知之甚少。该研究项目的目标是提供支持安全大数据操作的加密解决方案。为了能够专注于这里的加密挑战,但仍然为不同的架构提供有意义的解决方案来处理大数据,我们选择MapReduce框架作为我们构建的基础抽象层。因此,该项目的总体目标是使加密结构(如全同态加密)符合外包大数据的要求,并将加密解决方案纳入MapReduce框架。
英文摘要
Since Edward Snowden's disclosures of massive abuse of huge amounts of data by NSA and other agencies, it is evident that every individual and every company which cares about the privacy of their data has to take measures to protect the data. This is even more true for outsourced data in cloud storage and cloud computing scenarios, and when handling big data through third parties such as via Amazon's Elastic MapReduce. Standard cryptographic means such as encryption in general do not work here because by the very nature of encryption, scrambling all reasonable information, the semantics of the data are hidden and cannot be used by third parties to perform operations; the option of decrypting the data for the operations would violate the idea of protecting the data from the service provider.To reconcile the need for security with the ability to outsource computations we thus need cryptography which is compatible with the desired operations. In 2009 researchers from IBM announced a breakthrough result in cryptography by being able to build fully homomorphic encryption schemes which would allow such operations. However, little is known about the applicability of fully homomorphic encryption to large data sets. The goal of this research project is to provide cryptographic solutions which support operations on secured big data. To be able to focus on the cryptographic challenges here, but still providemeaningful solutions for different architectures to process big data, we choose the MapReduce framework as an abstract layer on which we base our constructions on. Hence, the overall goal of the project is to make cryptographic constructions such as fully homomorphic encryption fit the requirements of outsourced big data, and to incorporate the cryptographic solutions into the MapReduce framework.
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Scrutinizing Black-Box Separations in (Quantum) Cryptography
Scrutinizing Black-Box Separations in (Quantum) Cryptography
Minimizing Cryptographics Assumptions
Praktikable und beweisbar sichere Kryptographische Protokolle
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