Biblio

In the big data era, many users upload data to cloud while security concerns are growing. By using attribute-based encryption (ABE), users can securely store data in cloud while exerting access control over it. Revocation is necessary for real-world applications of ABE so that revoked users can no longer decrypt data. In actual implementations, however, revocation requires re-encryption of data in client side through download, decrypt, encrypt, and upload, which results in huge communication cost between the client and the cloud depending on the data size. In this paper, we propose a new method where the data can be re-encrypted in cloud without downloading any data. The experimental result showed that our method reduces the communication cost by one quarter in comparison with the trivial solution where re-encryption is performed in client side.

Network systems, such as transportation systems and water supply systems, play important roles in our daily life and industrial production. However, a variety of disruptive events occur during their life time, causing a series of serious losses. Due to the inevitability of disruption, we should not only focus on improving the reliability or the resistance of the system, but also pay attention to the ability of the system to response timely and recover rapidly from disruptive events. That is to say we need to pay more attention to the resilience. In this paper, we describe two resilience models, quotient resilience and integral resilience, to measure the final recovered performance and the performance cumulative process during recovery respectively. Based on these two models, we implement the optimization of the system recovery strategies after disruption, focusing on the repair sequence of the damaged components and the allocation scheme of resource. The proposed research in this paper can serve as guidance to prioritize repair tasks and allocate resource reasonably.

In this paper, a new approach based on Sub-sampled Inverse Fast Fourier Transform (SSIFFT) for efficiently acquiring compressive measurements is proposed, which is motivated by random filter based method and sub-sampled FFT. In our approach, to start with, we multiply the FFT of input signal and that of random-tap FIR filter in frequency domain and then utilize SSIFFT to obtain compressive measurements in the time domain. It requires less data storage and computation than the existing methods based on random filter. Moreover, it is suitable for both one-dimensional and two-dimensional signals. Experimental results show that the proposed approach is effective and efficient.

We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
 

Cover time measures the time (or number of steps) required for a mobile agent to visit each node in a network (graph) at least once. A short cover time is important for search or foraging applications that require mobile agents to quickly inspect or monitor nodes in a network, such as providing situational awareness or security. Speed can be achieved if details about the graph are known or if the agent maintains a history of visited nodes, however, these requirements may not be feasible for agents with limited resources, they are difficult in dynamic graph topologies, and they do not easily scale to large networks. This paper introduces a set-based form of heading (directional bias) that allows an agent to more efficiently explore any connected graph, static or dynamic. When deciding the next node to visit, agents are discouraged from visiting nodes that neighbor both their previous and current locations. Modifying a traditional movement method, e.g., random walk, with this concept encourages an agent to move toward nodes that are less likely to have been previously visited, reducing cover time. Simulation results with grid, scale-free, and minimum distance graphs demonstrate heading can consistently reduce cover time as compared to non-heading movement techniques.