To date, increasing number of entities on the smart grid begin to establish their local energy generator for ensuring reliability and resilience of power supply. These ‘microgrids’ can either connect to the power grid or isolate themselves from the grid by consuming their locally generated or stored energy. In reality, some microgrids may have excessive energy while the others may have to request extra energy from the main grid. To better balance the demand and supply of the distributed smart microgrids, it is desired to develop peer-to-peer (P2P) energy exchange models that enable microgrids to interactively exchange their local energy instead of consuming energy delivered from the main grid. However, in this scenario, all the microgrids have to disclose their private information (e.g., demand load and energy storage amount) to each other in the exchange. To tackle these issues, in this paper, we first formulate several novel energy exchange optimization problems that minimize the global energy loss during the exchange in different scenarios, and then develop an efficient and privacy-preserving scheme to solve the energy exchange optimization problems without private information disclosure. We also extend the privacy-preserving scheme to a collusion-resistant scheme in which all the microgrids cannot learn any additional information through colluding with each other. The performance of our proposed approaches is experimentally validated on real microgrid data. Copyright © 2015 John Wiley & Sons, Ltd.

Role mining is to define a role set to implement the role-based access control (RBAC) system and regarded as one of the most important and costliest implementation phases. While various role mining models have been proposed, we find that user experience/perception – one ultimate goal for any information system – is surprisingly ignored by the existing works. One advantage of RBAC is to support multiple role assignments and allow a user to activate the necessary role to perform the tasks at each session. However, frequent role activating and deactivating can be a tendinous thing from the user perspective. A user-friendly RBAC system is expected to assign few roles to every user. So in this paper we propose to incorporate to the role mining process a user-role assignment constraint that mandates the maximum number of roles each user can have. Under this rationale, we formulate user-oriented role mining as the user role mining problem, where all users have the same maximal role assignments, the personalized role mining problem, where users can have different maximal role assignments, and the approximate versions of the two problems, which tolerate a certain amount of deviation from the complete reconstruction. The extra constraint on the maximal role assignments poses a great challenge to role mining, which in general is already a hard problem. We examine some typical existing role mining methods to see their applicability to our problems. In light of their insufficiency, we present a new algorithm, which is based on a novel dynamic candidate role generation strategy, tailored to our problems. Experiments on benchmark data sets demonstrate the effectiveness of our proposed algorithm.

Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances’ energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficiently and effectively solve the ECS problems with large-scale inputs (which are NP-hard). The experimental results demonstrate that our approach is significantly more efficient than standard benchmarks, such as CPLEX, while guaranteeing near-optimal outputs.

To make the search engine more user-friendly, commercial search engines commonly develop applications to provide suggestion or recommendation for every posed query. Clustering semantically similar queries acts as an essential prerequisite to function well in those applications. However, clustering queries effectively is quite challenging, since they are usually short, incomplete and ambiguous. Existing prevalent clustering methods, such as K-Means or DBSCAN cannot guarantee good performance in such a highly dimensional environment. Through analyzing users’ click-through query logs, hierarchical agglomerative clustering gives good results but is computationally quite expensive. This paper identifies a novel feature for clustering search queries based on a key insight – queries’ top ranked search results can themselves be used to quantify query similarity. After investigating such feature, we propose a new similarity metric for comparing those diverse queries. This facilitates us to develop two very efficient and accurate algorithms integrated in query clustering. We conduct comprehensive experiments to compare the accuracy of our approach against the known baselines along two dimensions: 1) quantifying the cohesion/separation of clustered queries, and 2) justifying the results by real-world Internet users. The experimental results demonstrate that our two algorithms and the similarity metric can generate more accurate results within a significantly shorter time.