TWC: Small: Practical Assured Big Data Analysis in the Cloud

“云技术”的使用为大数据分析的需求提供了一条很有前途的途径。然而，安全问题是进一步采用云的主要障碍：通过共享云资源，在一个节点上成功的攻击可能会篡改共享该节点的多个应用程序。该项目探索了两种随时可用的实用机制的组合，以整体实现基于云的可靠大数据处理：(1)拜占庭容错复制和(2)部分同态加密。前者包括复制计算实体以实现可用性，并比较其产生的结果以加强结果的完整性以及隔离可疑组件。后者建议利用现有“密码系统”与生俱来的能力，支持对处于加密状态的数据进行某些特定操作，以确保其隐私。该项目设想了冗余计算(复制)和冗余存储(相同数据的不同加密)的有效应用，方法是根据程序分析确定的边界，将程序智能分解为子计算和子数据集。为了实现这一愿景，拜占庭容错复制的范围被扩展到当前的客户端-服务器方案之外，以避免在应用于大型数据集的细粒度并行时显著减慢；类似地，部分同态加密适用于不妨碍并行性的情况，并超出非常简单的程序。鉴于云计算范式和大数据的相关性不断增强，该项目将对软件开发人员产生很大影响。成果将通过科学出版物广泛提供，并使用开放源码软件系统加以实施。

The use of "cloud technologies" presents a promising avenue for the requirements of big data analysis. Security concerns however represent a major impediment to the further adoption of clouds: through the sharing of cloud resources, an attack succeeding on one node can tamper with many applications sharing that node. This project explores the combination of two readily-available, practical mechanisms to holistically achieve assured cloud-based big data processing: (1) Byzantine fault tolerant replication and (2) partially homomorphic encryption. The former consists in replicating computational entities to achieve availability, and comparing their produced results to enforce integrity of results as well as isolation of suspicious components. The latter suggests leveraging the innate ability of existing "cryptosystems" to support certain specific operations on data in encrypted state in order to ensure its privacy. The project envisions an efficient application of redundant computation (replication) and redundant storage (different encryptions of same data) through a smart breakdown of programs into sub-computations and sub-datasets based on boundaries identified via program analysis. To enable that vision, the scope of Byzantine fault tolerant replication is extended beyond the present client-server scenarios to avoid significant slowdowns when applied to fine-grained parallelization of large datasets; similarly, partially homomorphic encryption is made applicable without hampering parallelism and beyond very simple programs. This project will have a high impact on software developers given the continuously increasing relevance of the cloud computing paradigm and of big data. Results will be made broadly available through scientific publications and use open-source software systems for implementation.