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检索条件"机构=Anhui Province key Laboratory of Software Engineering in Computing and Communication"

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SvdNPD: a negative data publication method based on the sensitive value distribution

SvdNPD: a negative data publication method based on the sens...

引用

International Workshop on Artificial Immune Systems (AIS)

作者： Linli Wu Wenjian Luo Dongdong Zhao University of Science and Technology of China Hefei Anhui CN Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China

Existing data publication methods retain the relationship between the quasi-identifier attributes and sensitive attributes of published data. We call them "positive data publication". However, it will lead to potential risk that attackers could deduce the privacy of the corresponding individuals from the published data. Recently, by combining the negative representation with k-anonymity and l-diversity, (k, m)- anonNPD and (l, m)-divNPD algorithms have been proposed, which improve the degree of privacy because the sensitive value of each individual is transformed into a negative value randomly. However, to reduce the average error of the reconstructed distribution, the data published by (k, m)-anonNPD and (l, m)-divNPD are often much larger than the original data. In this paper, we take the Sensitive Value Distribution into consideration, and propose a novel Negative Data Publication approach (i.e. SvdNPD), which is based on l-diversity. According to the sensitive value distribution, the negative selection probability of the sensitive attribute in each record can be calculated. Then the data could be published by transforming the sensitive value to a negative one with this probability. Experimental results show that the effective usage of the individual sensitive value distribution could enhance the utility of published data. Meanwhile, compared with (l, 1)-divNPD, SvdNPD just sacrifices a tiny privacy degree which is acceptable.

关键词： Data privacy Privacy Diseases Aggregates Databases Lungs Organizations

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PN-HASH: An Immune-Inspired Scheme for Data Integrity Check

PN-HASH: An Immune-Inspired Scheme for Data Integrity Check

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Junteng Wang Wenjian Luo Yamin Hu Hao Jiang Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

Cryptographic hash functions play significant roles in information security applications, including data integrity. Facing hash collision attacks, several prevalent cryptographic hash functions have been proved not safe enough. In this paper, we propose a simple scheme for data integrity check called PN-HASH (Positive hash and Negative hash). PN-HASH is inspired by the negative representation of information, which is a research branch in the field of immune computation. This scheme computes two hash values of the input message and its negative representation. The resulting hashes are called the positive hash and the negative hash, respectively. The proposed scheme is simple but can improve the resistance to hash collision attacks. Furthermore, for more flexibility and higher security, we propose three extensions of PN-HASH. Security and complexity analyses show that PN-HASH could improve the security of underlying cryptographic hash functions, with approximately twice cost of them. To give exact examples, we present PN-MD5, an implementation of PN-HASH based on MD5.

关键词： Cryptography Data integrity Databases Resistance Complexity theory Face

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AN APPROACH BASED ON TSA-TREE FOR ACCURATE TIME SERIES CLASSIFICATION

AN APPROACH BASED ON TSA-TREE FOR ACCURATE TIME SERIES CLASS...

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2012 IEEE 2nd International Conference on Cloud computing and Intelligence Systems

作者： Xiaoxu He Chenxi Shao School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication

In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Surprise Abstraction tree （TSA-tree）. The second step of the approach is to combine the exacted global feature and 1 nearest neighbor to classify time series. The proposed approach is compared with a number of known classifiers by experiments in artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.

关键词： Feature exaction dimension reduction classification TSA-tree

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A hormone based tracking strategy for wireless sensor network

A hormone based tracking strategy for wireless sensor networ...

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IEEE International Conference on Intelligent computing and Intelligent Systems (ICIS)

作者： Yipeng Zhang Haifeng You Xufa Wang Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology Hefei China

In this paper, we present a hormone based tracking strategy called Hormone based Activation (HA) for mobile target tracking in wireless sensor networks inspired by the bio-endocrine system. Hormone messages are used in sensor networks to coordinate sensor nodes for mobile target tracking in a self-adaptive way. Three kinds of hormones are introduced into sensor network for mobile target tracking: Base Hormone (BH),Wakeup Hormone (WH) and Sleep Hormone (SH).Based on different hormone messages, sensor nodes can choose to be asleep or awake in order to get good tracking results with less energy consumed. Comparative experiment shows that our strategy needs fewer nodes to wake up and has a much higher usage ratio of sensor nodes than other tracking strategies.

关键词： Biochemistry Wireless sensor networks Target tracking Biosensors Intelligent sensors Computer networks Mobile communication Sleep Energy efficiency communication system control

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A Clustering System for Gene Expression Data Based upon Genetic Programming and the HS-Model

A Clustering System for Gene Expression Data Based upon Gene...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Guiquan Liu Xiufang Jiang Lingyun Wen Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9781424468126;9780769540306

Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset

关键词： genetic programming cluster analysis missing value fitness function

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A fast algorithm for finding community structure based on community closeness

A fast algorithm for finding community structure based on co...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Xiufang Jiang Guiquan Liu Zhiting Lin Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9781424468126;9780769540306

Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a /ast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk + mt), where k denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding.

关键词： community structure complex network data mining community closeness

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Directed differential evolution based on directional derivative for numerical optimization problems

Directed differential evolution based on directional derivat...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Jun Zhang Wenjian Luo School of Computer Science and Technology University of Science and Technology Hefei Anhui China Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology Hefei Anhui China

Differential Evolution is one kind of Evolutionary Algorithms, which has been successfully applied to solve many optimization problems. In this paper, a directed differential mutation (DDM), which utilizes the directional derivative to decide a suitable search direction and a proper mutation step size, is proposed. It is merged into the classical DE to form a new algorithm, named directed differential evolution (DDE). The performance of the DDE is tested on 23 classical problems for numerical optimization. The experimental results demonstrate that the performance of the DDE outperforms the classical DE on most functions.

关键词： Optimization Vectors Equations Hybrid intelligent systems Algorithms Programming Maintenance engineering

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Extracting Qualitative States from Nonlinear Time Series Using Integration of Fuzzy c-Means and Hierarchical Clustering

Extracting Qualitative States from Nonlinear Time Series Usi...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chenxi Shao Jinfeng Fan Xiaoping Chen Department of Computer Science and Technology University of Science and Technology of China Hefei Anhui China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui China

A novel method combined fuzzy c-means (FCM) and hierarchical agglomerative clustering (HAC) is presented to extract the qualitative states from reconstructed phase space by considering the dynamical relations of underlying nonlinear system. The qualitative state, in which all points have similar properties, is characterized by three measures, linear density, temporal reachability, and stable degree, which are defined as the criteria of merging subclasses. The time series sampled from a classic nonlinear system, Lorenz system, and electroencephalogram (EEG) signals are used to test this method. The results show that the qualitative states can be efficiently distinguished from these time series by the proposed method.

关键词： Space technology Nonlinear systems Clustering methods Fuzzy systems Merging Electroencephalography Chaotic communication Delay Computer science Laboratories

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A fine-grained algorithm for generating hard-toreverse negative databases

A fine-grained algorithm for generating hard-toreverse negat...

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International Workshop on Artificial Immune Systems (AIS)

作者： Dongdong Zhao Wenjian Luo Ran Liu Lihua Yue Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China School of Computer Science China University of Geosciences Wuhan China

The negative database (NDB) is a new technique for privacy preserving and information hiding. It hides information by storing the complementary set instead of the original data. In order to protect the hidden information, NDBs should be hard-to-reverse. In this paper, we propose the K-hidden algorithm for generating hard-to-reverse NDBs (called K-hidden-NDBs). The K-hidden algorithm could be controlled in a more fine-grained manner than existing NDB generation algorithms. Moreover, in terms of the SAT solvers based on the local search strategy, we formally prove that the K-hidden-NDBs could be more hard-to-reverse than the NDBs generated by the typical p-hidden algorithm. Furthermore, we show that the K-hidden-NDB could be more hard-to-reverse (against the local search strategy) than the q-hidden-NDB (NDBs generated by the q-hidden algorithm) when the sizes of NDBs are the same. Finally, as for the Unit Clause heuristic solvers, we prove that the K-hidden-NDB could be the same hard-to-reverse as the q-hidden-NDB.

关键词： Search problems Databases Security Data privacy Algorithm design and analysis Computer science NP-hard problem

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Coverage-preserving Data Gathering in Wireless Sensor Networks

Coverage-preserving Data Gathering in Wireless Sensor Networ...

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Hongli Xu Liusheng Huang Jumin Wu Yang Wang Depart. of Computer Science and Technology Anhui Province key Laboratory of Software in Computing and Communication University of Science and Technology Hefei China

The power is the most significant resource in wireless sensor nodes, so energy conservation is the most important factor in designing the protocol for sensor networks. In this paper, we study the problem of energy-efficient collection of the physical information from the target area. The coverage-preserving data gathering (CPDG) problem is to build up a tree, which minimizes the total energy consumption, and preserves the original coverage to ensure the result correctness. This problem can be formalized as 0-1 integer linear programming, and solved by an efficient heuristic algorithm. Furthermore, an improved algorithm is also proposed to balance the energy consumption among all the sensor nodes. The experimental results show that CPDG algorithm can conserve the energy about 30% of PEDAP protocol, which is regarded as one of the most efficient data gathering protocols as we know.

关键词： Wireless sensor networks Energy efficiency Energy consumption Heuristic algorithms Monitoring Distributed computing Computer networks Energy conservation Wireless application protocol Integer linear programming

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