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: --
• 依托单位: Lehrstuhl Kryptologie und IT-Sicherheit
• 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
• 起止时间: 2013-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/255319481?language=en
• 关键词: SecOBig; Security; Preserving; Operations; Big

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.

由于爱德华·斯诺登（Edward Snowden）披露了NSA和其他机构对大量数据的大量滥用披露，因此很明显，每个关心数据隐私的个人和每个公司都必须采取措施来保护数据。对于云存储和云计算方案中的外包数据，以及通过第三方（通过亚马逊的弹性MapReduce）处理大数据时，这是更正确的。标准加密含义诸如加密通常不起作用，因为通过加密的性质，拼凑出所有合理的信息，数据的语义被隐藏，第三方无法使用该操作；解密操作数据的选项将违反保护数据免受服务提供商的想法。为了使安全需求与外包计算的能力调和，我们需要与所需操作兼容的密码学。 2009年，来自IBM的研究人员宣布了一个突破性的结果，即能够构建完全允许此类操作的完全同构加密方案。但是，对于完全同态加密对大型数据集的适用性知之甚少。该研究项目的目的是提供密码解决方案，以支持有安全的大数据进行操作。为了能够关注这里的加密挑战，但是对于不同架构以处理大数据的各种构造的解决方案仍在进行中，我们选择了MapReduce框架作为一个抽象层，我们将其基于构造的基础。因此，该项目的总体目标是使诸如完全同构加密之类的加密结构符合外包大数据的要求，并将加密解决方案纳入MapReduce框架。