With the rapid development of cloud computing, more and more users store their data in the cloud. Considering the privacy problem of the data sharing in the cloud, searchable encryption makes it possible to efficientl...
With the rapid development of cloud computing, more and more users store their data in the cloud. Considering the privacy problem of the data sharing in the cloud, searchable encryption makes it possible to efficiently share and retrieve data in multi-user settings. The existing keyword search scheme of sharing the private key with authorized users to generate trapdoors may increase the risk of key exposure, and the scheme of encrypting the data with the secret key of each authorized user and generating a ciphertext for each user may not be suitable for data sharing between a large number of users. In this paper, we combine the secure IBBE system with the keywords search and propose the scheme to achieve safe and efficient multi-user data sharing in the cloud. Authorized users can perform keyword search directly on encrypted data without sharing the secret key, and for a shared file, the data owner only needs to encrypt one ciphertext for all authorized users. Besides, authorized users of shared files can be revoked. User authorization tables and IBBE ensure that revoked users cannot access files.
Subgraph query is an important problem in the research and application of large graph *** large graphs with symmetry relation substructures,the existing decomposition-join strategy always leads to low searching *** so...
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Subgraph query is an important problem in the research and application of large graph *** large graphs with symmetry relation substructures,the existing decomposition-join strategy always leads to low searching *** solve this problem,we proposed a new decomposition-Detection-join strategy,in which we detect symmetric relations of each sub part of the decomposition,and then determine the sequence of queries based on the detection *** experimental results show that the algorithm has much improvement in query efficiency.
Feature selection based on information theory plays an important role in classification algorithm due to its computational efficiency and independent from classification method. It is widely used in many application a...
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Feature selection based on information theory plays an important role in classification algorithm due to its computational efficiency and independent from classification method. It is widely used in many application areas like data mining, bioinformatics and machine learning. But drawbacks of these methods are the neglect of the feature interaction and overestimation of features significance due to the limitations of goal functions criterion. To address this problem, we proposed a new feature goal function RJMIM. The method employed joint mutual information and information interaction, which alleviates the shortcomings of overestimation of the feature significance as demonstrated both theoretically and experimentally. The experiments conducted to verify the performance of the proposed method, it compared with four well-known feature selection methods use three publically available datasets from UCI. The average classification accuracy and C4.5 classifier is used to assess the effectiveness of RJMIM method.
With the increasing number of GPS-equipped vehicles, more and more trajectories are generated continuously, based on which some urban applications become feasible, such as route planning. In general, route planning ai...
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The popularity of GPS-embedded devices facilitates online monitoring of moving objects and analyzing movement behaviors in a real-time manner. Trajectory clustering acts as one of the most important trajectory analysi...
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Taxi-sharing is an efficient way to improve the utility of taxis by allowing multiple passengers to share a taxi. It also helps to relieve the traffic jams and air pollution. It is common that different users may have...
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With the advent of software-as-a-Service (SaaS), SaaS developers are facing many challenges associated with the multi-tenancy and the dramatically increased number of users. In order to achieve resource-optimized, on-...
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ISBN:
(纸本)9781538637913
With the advent of software-as-a-Service (SaaS), SaaS developers are facing many challenges associated with the multi-tenancy and the dramatically increased number of users. In order to achieve resource-optimized, on-demand dynamic scaling across multiple tenants, and reduce costs, in this paper, a new platform, named SmartVM, is created to enable SaaS developer to create, customize, and deploy SaaS solutions in a multi-tier microservice-based manner. We develop an e-commerce SaaS prototype to evaluate effectiveness and efficiency of SmartVM. The results show that the SmartVM deployments outperforms the conventional monolithic and microservice deployments in smart monitoring, cost reduction, and resource optimization.
Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank (PPR...
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Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank (PPR) and Sim- Rank (SR) have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank (P-Rank), which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition: the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.
Sorted list is widely used to feature indexing in a variety of applications, such as multimedia database and information retrieval. Answering top-k aggregation queries on a set of lists plays an increasingly important...
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Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditi...
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ISBN:
(纸本)9781538637913
Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditional methods. An improved parallel ACO algorithm based on pattern learning is proposed in this paper. It extracts parameters automatically to reduce solution space and enhance calculation efficiency. Various parameters in the algorithm are analyzed, and a refining strategy is formed according to ACO's characteristics. The parallel ACO algorithm is carried out under the MIC/CPU architecture, and it can significantly enhance performance.
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