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以篡改共享该节点的许多应用程序。本项目探索了两种容易获得的实用机制的结合，以整体实现有保证的基于云的大数据处理：(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，并比较它们产生的结果以加强结果的完整性以及对可疑组件的隔离。后者建议利用现有“密码系统”的固有能力来支持对加密状态下的数据进行某些特定操作，以确保其隐私。该项目设想通过基于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，有效地应用冗余计算（复制）和冗余存储（相同数据的不同加密）。为了实现这一愿景，拜占庭容错复制的范围扩展到当前的客户机-服务器场景之外，以避免在应用于大型数据集的细粒度并行化时出现明显的减速；类似地，部分同态加密在不妨碍并行性和非常简单的程序之外也适用。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重大影响。结果将通过科学出版物广泛提供，并使用开源软件系统进行实施。

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.