Speculative reordering for a latency-optimized privacy protection in complex event processing

With increasing number of applications in Internet of Things (IoT), Complex Event Processing (CEP) has already become one of the state-of-the-art technologies recently. In CEP, privacy needs to be considered carefully because events with user’s sensitive information may be exposed to outside world. However, most privacy issues in CEP mainly focus on attribute-based events without considering pattern-based events. There are two important works for pattern-based privacy in CEP: suppression and re-ordering. The former suppresses events belonging to private patterns while the later tends to reorder them. The re-ordering mechanism shows better performance in terms of QoS, but the latency would be long when the size of window increases. Also, the re-ordering strategy is performed only at the end of the windows. In this thesis, we extend the Re-ordering strategy by using speculation based on Markov chains, so we start speculating whether the private pattern occurs in current window before the end of the window. If the private pattern is predicted to occur, we then already re-order events that are part of private patterns. Additionally, the top-k preserving algorithm is introduced for preserving public patterns. Our evaluation results show that we maintain nearly 80 % utility when compared to the normal re-ordering strategy. From our experiments, it is seen that we can eliminate the time taken for re-ordering completely if the window size is greater than 3 ms

Multilevel Secure Data Stream Processing

With sensors and mobile devices becoming ubiquitous, situation monitoring applications are becoming a reality. 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 investigate the issues important in an MLS DSMS and propose an architecture that best meets the goals of MLS DSMS. We discuss how continuous queries can be executed in such a system and sharing across queries accomplished for maximum performance benefits