CSR: Small: Collaborative Research: Multi-party Collaborative Data Access

随着各种各样的企业和组织所维护的数据的丰富性和数量的增加，越来越需要获取有用的信息，不仅是从单个方拥有的数据中获取，而且是从跨方拥有的数据中获取。目前的数据共享实践主要是由组织之间精心协商的、大多是保密的一对一协议主导的，而且受到严重限制。它不能随着数据库和组织数量的增加而扩展，不能对意外的信息泄漏或其他违规行为进行正式分析，并且可能需要不必要的数据公开。该项目探索了一种协作多方数据共享机制，该机制允许安全方便地共享数据库，从而实现大规模协作数据分析和知识提取应用。多方数据共享涉及项目试图解决的许多挑战。首先，有必要为各方的数据访问制定一个直观的访问控制策略规范，并建立一个自动机制，在解决冲突、规范不足和其他问题的同时，将这些访问控制策略转换为一组简洁的访问规则。其次，尽管存在复杂的访问限制，我们仍然需要对允许的查询进行高效和可伸缩的实现。第三，既要处理传统的关系数据库及其变体，又要处理新兴的图数据库。最后，我们需要有效检查作为答案提供的数据的完整性和完整性的机制。通过解决这些挑战，本研究希望为多方数据共享奠定基础。本研究中的许多形式化将利用基于可满足模理论（Satisfiability Modulo Theories， SMT）的方法来测试断言和解决冲突。由于网络和网络物理系统都会产生大量数据，该项目将有助于设计实用的机制，在各种情况下共享数据，从而加快数据驱动创新应用于服务社会的信息系统的步伐。

As the richness and volume of data maintained by a wide variety of businesses and organizations increases, there is a growing need to derive useful information, not only from data owned by individual parties, but also across parties. The current data sharing practice is dominated by carefully negotiated, mostly secret, one-on-one agreements between organizations, and is seriously limited. It does not scale as the number of databases and organizations increase, cannot be formally analyzed for unintended information leaks or other violations, and may require unnecessary data exposure. This project explores a collaborative multi-party data sharing mechanism that allows safe and convenient sharing of databases in order to enable large-scale collaborative data analytics and knowledge extraction applications. Multi-party data sharing involves many challenges that the project attempts to address. First, it is necessary to have an intuitive specification of access control policies for data access across parties and an automated mechanism to translate those into a concise set of access rules while resolving conflicts, specification inadequacies, and other problems. Second, we need efficient and scalable implementation of allowed queries in spite of complex access restrictions. Third, it is necessary to handle both the traditional relational databases, its variants, and the emerging graph databases. Finally, we need mechanisms to efficiently check the integrity and completeness of the data supplied as answers. By addressing these challenges, the research hopes to establish the foundations of multiparty data sharing. Many of the formalizations in this research will exploit Satisfiability Modulo Theories (SMT) based methods for testing assertions and for resolving conflicts. With tremendous amount of data being generated by both cyber and cyberphysical systems, this project will help devise practical mechanisms to share data in a wide variety of scenarios and thereby accelerate the pace of data driven innovation applied to information systems serving the society.