Abstract: In recent years, the rapid expansion of the capability of portable devices, cloud servers and cellular network technologies is the wind beneath the wing of mobile social networks. Compared to traditional web-based online social networks, the mobile social networks can assist users to easily discover and make new social interaction with others. A challenging task is to protect the privacy of the users' profiles and communications. Existing works are mainly based on traditional cryptographic methods, such as homomorphic and group signatures, which are very computationally costly. In this paper, we propose a novel distributed multi-authority attribute-based encryption scheme to efficiently achieve privacy-preserving without additional special signatures. In addition, the proposed scheme can achieve fine-grained and flexible access control. Detailed analysis demonstrates the effectiveness and practicability of our scheme.

Abstract: In the converging world of High Performance Computing and Big Data, moving data is becoming a critical aspect of performance and energy efficiency. In this paper we present the Advanced DAta Movement Analysis Toolkit (ADAMANT), a set of tools to capture and analyze data movement within an application, and to aid in understanding performance and efficiency in current and future systems. ADAMANT identifies all the data objects allocated by an application and uses instrumentation modules to monitor relevant events (e.g. cache misses). Finally, ADAMANT produces a per-object performance profile. In this paper we demonstrate the use of ADAMANT in analyzing three applications, BT, BFS, and Velvet, and evaluate the impact of different memory technology. With the information produced by ADAMANT we were able to model and compare different memory configurations and object placement solutions. In BFS we devised a placement which outperforms caching, while in the other two cases we were able to point out which data objects may be problematic for the configurations explored, and would require refactoring to improve performance.

Abstract: Urgent computing requires computations to commence in short order and complete within a stipulated deadline so as to support mitigation activities in preparation, response and recovery from an event that requires immediate attention. Missing an urgent deadline can lead to dire consequences where avoidable human and financial losses ensued. Allocation of resources such that the deadline can be met is thus crucial. Robustness is of great importance to ensure that small perturbations on the computing systems do not affect the makespan of computations such that the deadline is missed. This work focuses on developing a general mathematical makespan model for urgent computing to enable a robust allocation of ensemble forecasts while minimising the makespan. Three patterns of different resource allocation will be investigated to illustrate the model. The result will aid in satisfying the most crucial requirement, the time criterion, of urgent computing.