Workflow security has become increasingly important and challenging in today's open service world. While much research has been conducted on various security issues of workflow systems, the workflow satisfiability problem, which asks whether a set of users together can complete a workflow, is recently identified as an important research problem that needs more investigation. In this paper, we study the computational complexity of the problem along two directions: one is by considering either one path or all paths of a workflow, and the other is by considering the possible patterns in a workflow. We have shown that the general workflow satisfiability analysis problem is intractable. This result motivates us to consider restrictions on workflow control-flow patterns and access control policies, and to identify tractable cases of practical interest.

In the near future, clouds will provide situational monitoring services using streaming data. Examples of such services include health monitoring, stock market monitoring, shopping cart monitoring, and emergency control and threat management. Offering such services require securely processing data streams generated by multiple, possibly competing and/or complementing, organizations. Processing of data streams also should not cause any overt or covert leakage of information across organizations. We propose an information flow control model adapted from the Chinese Wall policy that can be used to protect against sensitive data disclosure. We propose architectures that are suitable for securely and efficiently processing streaming information belonging to different organizations. We discuss how performance can be further improved by sharing the processing of multiple queries. We demonstrate the feasibility of our approach by implementing a prototype of our system and show the overhead incurred due to the information flow constraints.

The proliferation of sensors and mobile devices and their connectedness to the network have given rise to numerous types of situation monitoring applications. Data Stream Management Systems (DSMSs) have been proposed to address the data processing needs of such applications that require collection of high-speed data, computing results on-the-fly, and taking actions in real-time. Although a lot of work appears in the area of DSMS, not much has been done in multilevel secure (MLS) DSMS making the technology unsuitable for highly sensitive applications, such as battlefield monitoring. An MLS–DSMS should ensure the absence of illegal information flow in a DSMS and more importantly provide the performance needed to handle continuous queries. We illustrate why the traditional DSMSs cannot be used for processing multilevel secure continuous queries and discuss various DSMS architectures for processing such queries. We implement one such architecture and demonstrate how it processes continuous queries. In order to provide better quality of service and memory usage in a DSMS, we show how continuous queries submitted by various users can be shared. We provide experimental evaluations to demonstrate the performance benefits achieved through query sharing.