Mingzhong Wang

Computationally sound symbolic security reduction analysis of the group key exchange protocols using bilinear pairings

The security of the group key exchange protocols has been widely studied in the cryptographic community in recent years. Current work usually applies either the computational approach or the symbolic approach for security analysis. The symbolic approach is more efficient than the computational approach, because it can be easily automated. However, compared with the computational approach, it has to overcome three challenges: (1) The computational soundness is unclear; (2) the number of participants must be fixed; and (3) the advantage of efficiency disappears, if the number of participants is large. This paper proposes a computationally sound symbolic security reduction approach to resolve these three issues. On one hand, combined with the properties of the bilinear pairings, the universally composable symbolic analysis (UCSA) approach is extended from the two-party protocols to the group key exchange protocols. Meanwhile, the computational soundness of the symbolic approach is guaranteed. On the other hand, for the group key exchange protocols which satisfy the syntax of the simple protocols proposed in this paper, the security is proved to be unrelated with the number of participants. As a result, the symbolic approach just needs to deal with the protocols among three participants. This makes the symbolic approach has the ability to handle arbitrary number of participants. Therefore, the advantage of efficiency is still guaranteed. The proposed approach can also be applied to other types of cryptographic primitives besides bilinear pairing for computationally sound and efficient symbolic analysis of group key exchange protocols.

Dependency-based Risk Evaluation for Robust Workflow Scheduling

The robustness of a schedule, with respect to its probability of successful execution, becomes an indispensable requirement in open and dynamic service-oriented environment, such as grids or clouds. We design a fine-grained risk assessment model customized for workflows to precisely compute the cost of failure of a schedule. In comparison with current course-grained model, ours takes the relation of task dependency into consideration and assigns higher impact factor to tasks at the end. Thereafter, we design the utility function with the model and apply a genetic algorithm to find the optimized schedule, thereby maximizing the robustness of the schedule while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk assessment model into scheduling can generally improve the successful probability of a schedule while reducing its exposure to the risk.

Robust Scheduling and Runtime Adaptation of Multi-agent Plan Execution

Robustness and reliability with respect to the successful completion of a schedule are crucial requirements for scheduling in multi-agent systems because agent autonomy makes execution environments dynamic and nondeterministic. We introduce a model to incorporate trust which indicates the probability that an agent will comply with its commitments into scheduling, thus improving the predictability and stability of the schedule. To deal with exceptions during execution, we adapt and evolve the schedule at runtime by interleaving the processes of evaluation, scheduling, execution and monitoring in the life cycle of a plan. Experiments show that schedules maximizing participantspsila trust are more likely to survive and succeed in open and dynamic environments. The results also prove that the proposed plan evaluation approach conforms with the simulation result, thus being helpful for plan selection.

Trust-based workflow refactoring for concurrent scheduling in service-oriented environment

Workflow scheduling has been extensively studied to improve the system performance. However, existing approaches are usually built on predefined workflow graph structure, neglecting the possibility that a workflow graph itself may be changeable when certain conditions are satisfied. Therefore, in this paper, we propose the concept of graph refactoring that transforms certain types of sequential tasks to run in parallel without changing systems functionality. We first provide a classification for task dependencies in workflows and identify that previously sequential task ordering in loose control dependency can be scheduled to run in parallel as long as supporting services are trustworthy. With this concept, we present a refactoring algorithm to traverse, restructure, and parallelize loose control dependencies in the graph when the reputations of related executing services are above certain threshold. In addition, refactoring effects on common sub‐graph structures are analyzed and discussed. In practice, our algorithm can be integrated into existing workflow management systems as a preprocessor to generate a new functionally equivalent working graph with more concurrent branches for further scheduling. Experiments and analysis show that graph refactoring can improve the system performance scalably because of concurrent execution of previously sequential tasks. Copyright

ARTS: agent-oriented robust transactional system

This paper presents the ARTS (Agent-oriented Robust Transactional System) model, which applies transaction concepts to provide agent developers with high-level support for agent system robustness and reliability. ARTS abstractly considers agents as executors of encapsulated task entities which comply with a set of execution constraints on both normative execution and compensation (repair) semantics. ARTS then defines the task interface in terms of predictable terminating states to support a contract-like interaction among agents. In conjunction with this encapsulation of task semantics, ARTS defines a model for specifying scoped compensation and exception-handling plans for a given task, and for systematically selecting and executing these plans --- triggered by subtask events --- so that the enclosing task semantics are enforced. These capabilities together define a model that reduces design complexity while increasing system robustness, by allowing an agent developer to compose recursively-defined, atomically-handled tasks.

Risk-aware intermediate dataset backup strategy in cloud-based data intensive workflows

Data-intensive workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we introduce the risk evaluation model tailored for workflow structure to measure and achieve the trade-off between the overhead of backup storage and the cost of data regeneration in failure, making the service selection and execution more efficient and robust. The proposed method computes and compares the potential loss with and without data backup to achieve the trade-off between overhead of intermediate dataset backup and task re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the volume of generated data is not large in comparison with the input. Introduce the risk evaluation model for workflow to measure potential loss.Propose the intermediate dataset backup strategy.Achieve tradeoff between the overhead of backup and re-execution after exceptions.Apply a genetic algorithm to find reliable and cost-effective selection of services.Compares the potential loss with and without our data backup strategy.

Reasoning task dependencies for robust service selection in data intensive workflows

Selecting appropriate services for task execution in workflows should not only consider budget and deadline constraints, but also ensure the best probability that workflow will succeed and minimize the potential loss in case of exceptions. This requirement is more critical for data-intensive applications in grids or clouds since any failure is costly. Therefore, we design a fine-grained risk evaluation model customized for workflows to precisely compute the cost of failure for selected services. In comparison with current course-grained model, ours takes the relation of task dependency into consideration and assigns higher impact factor to tasks at the end. Thereafter, we design the utility function with the model and apply a genetic algorithm to find the optimized service allocations, thereby maximizing the robustness of the workflow while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk evaluation model into service selection can generally improve the successful probability of a workflow while reducing its exposure to the risk.

Risk-Aware Checkpoint Selection in Cloud-Based Scientific Workflow

Scientific workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we extend the risk evaluation model, which assigns different weights to tasks based on their ordering relationship, to decide the occasion to perform backup or checkpoint service after the completion of a task. The proposed method computes and compares the potential loss with and without data backup to achieve the tradeoff between overhead of check pointing and re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the generated data is not large.

Content integrity and non‐repudiation preserving audio‐hiding scheme based on robust digital signature

Current secure communication schemes do not take together traffic security and data security (content integrity and non-repudiation) of the secret message into consideration, making the content prone to blind tampering and compromised party cheating attacks. In this paper, we present a scheme that hides secret audio in cover audio on the basis of robust digital signature to preserve not only hidden communication but also content integrity and non-repudiation of the secret audio. Furthermore, instead of traditional binary authentication that only outputs yes or no, the authentication of our scheme is flexibly measurable, and the measurement value is in correspondence with the sense of human hearing precisely. Experimental results show that the proposed scheme provides highly robust authentication against content-preserving degradations with 99.03% of test audios having the strongest authenticity (1.00) and high level of distinct authentication between content-destructive degradations with 95.01% of test audios having relatively weak authenticity (less than 0.15). As the authentication is flexibly measureable, there is no false alarm in the semantic aspect. Copyright

Workflow Refactoring for Concurrent Task Execution

The performance and reliability of workflow execution are highly dependent on the scheduling algorithm. However, existing approaches usually confines the scheduling on the predefined workflow structure, neglecting the possibility that a workflow graph itself may be changeable when certain conditions are satisfied. Therefore, in this paper we propose the concept of graph refactoring which transforms certain types of sequential tasks to run in parallel without changing systems functionality. We first propose a classification of task dependencies in DAG-style workflow graphs as data, strict control, and loose control dependency according to task interaction and user requirements, and identify that previously sequential task ordering in loose control dependency can be scheduled to run in parallel as long as supporting services are trustworthy. Corresponding refactoring algorithms are designed to traverse, restructure, and parallelize loose control dependencies in the graph when the reputations of related executing services are above certain threshold. Experiments and analysis show that graph refactoring can improve the system performance scalably because of concurrent execution of previously sequential tasks.