10813806
An efficient protocol for anonymous and fair document exchange
Fairness in document exchange has been well studied, while anonymity in the exchange, which protects the privacy of personal information such as identities and locations, has been either ignored or handled with partial or inappropriate considerations. In this paper we propose a new protocol for anonymous and fair document exchange between two parties with the assistance of an off-line trusted third party. The new protocol treats both fairness and anonymity as essential properties, employs an efficient method for off-line key recovery, and places weak requirements on the security of the third party.
13044783
A New Time-domain Detection Approach with Blind Separation Based on Neural Network
In this paper a new technique is proposed for time-domain detection in energy detection. The proposed scheme is based on a independent-component analysis (ICA): blind separation of unknown source signals. This method not only improves the nicety of time-domain detection for narrow band signal and the sinusoidal signal but also reduces the power of the whole detection system.
15488270
Segmentation of Spectrum Map for HFSWR Based on Feature Extraction
When targets fall in different types of clutter and noise background in High Frequency Surface Wave Radar (HFSWR), uniform detection algorithms always result in poor detection and tracking performances. To solve the problem, two methods are proposed in this paper by extracting the background features according to the probability distribution and the image characteristic of the spectrum map. The first method uses Parzen window for estimating probability density function and Kullback-Leibler convergence for evaluating the difference between the Azimuth-Range-Doppler cells in spectrum map which can be used to distinguish the homogenous detection background from the non-homogenous. The second method is proposed based on the image edge feature in multi-scale space. The real data results about HFSWR show that the proposed methods can not only identify noise/clutter but also help choose appropriate detection/tracking algorithms to improve performances in such complex environments.
14935404
Levels of Authentication Assurance: an Investigation
The ES-LoA project, funded by the UK Joint Information Systems Committee (JISC) under its e- Infrastructure Security Programme, investigates current and future needs among UK research and education community for a more fine-grained authorisation scheme that would allow service providers to take into account of the levels of confidence in identifying a remote entity requesting for service access. Such a fine-grained authorisation scheme is attractive to service providers offering resources with varying levels of sensitivity and/or wishing to tailor their security protections based upon risk levels. Service providers may wish to restrict access to more sensitive resources only to those who have gone through a more stringent authentication process, or given the same remote entity, require the use of a stronger authentication token should the access request come from a more risky environment. In this way, the quality of an authentication instance, expressed as an authentication Level of Assurance (LoA), becomes one of the parameters used in access control decision making. This paper investigates the current worldwide efforts in defining LoA and identifies gaps in existing definitions when they are applied to a federated environment.
14654466
A Purpose-Based Access Control Model
Achieving privacy preservation in a data-sharing computing environment is a challenging problem. The requirements for a privacy preserving data access policy should be formally specified in order to be able to establish consistency between the privacy policy and its purported implementation in practice. Previous work has shown that when specifying a privacy policy, the notion of purpose should be used as the basis for access control. A privacy policy should ensure that data can only be used for its intended purpose, and the access purpose should be compliant with the data's intended purpose. This paper presents a mechanism to specify privacy policy using VDM. The entities in the purpose-based access control model are specified, the invariants corresponding to the privacy requirements in privacy policy are specified, and the operations in the model and their proof obligations are defined and investigated.
42027884
New Approximation for Minimum-Weight Routing Backbone in Wireless Sensor Network
Our problem formulation is as follows. Given a weighted disk graph Gwhere the weight of edge represents the transmission energy consumption, we wish to determine a dominating tree Tof Gsuch that the total weight of edges in Tis minimized. To the best of our knowledge, this problem have not been addressed in the literature. Solving the dominating tree problem can yield a routing backbone for broadcast protocols since: (1) each node does not have to construct their own broadcast tree, (2) utilize the virtual backbone to reduce the message overhead, and (3) the weight of backbone is minimized. 
 
Our contributions to this problem is multi-fold: First, the paper is the first to study this problem, prove the hardness of this problem and propose an approximation framework. Second, we present a heuristic to approximate the solution with low time complexity. Third, a distributed algorithm is provided for practical implementation. Finally, we verify the effectiveness of our proposal through simulation.
5525123
FIX: feature-based indexing technique for XML documents
Indexing large XML databases is crucial for efficient evaluation of XML twig queries. In this paper, we propose a feature-based indexing technique, called FIX, based on spectral graph theory. The basic idea is that for each twig pattern in a collection of XML documents, we calculate a vector of features based on its structural properties. These features are used as keys for the patterns and stored in a B+tree. Given an XPath query, its feature vector is first calculated and looked up in the index. Then a further refinement phase is performed to fetch the final results. We experimentally study the indexing technique over both synthetic and real data sets. Our experiments show that FIX provides great pruning power and could gain an order of magnitude performance improvement for many XPath queries over existing evaluation techniques.
18987585
Efficient elliptic curve scalar multiplication algorithms resistant to power analysis
This paper presents four algorithms for securing elliptic curve scalar multiplication against power analysis. The highest-weight binary form (HBF) of scalars and randomization are applied to resist power analysis. By using a special method to recode the scalars, the proposed algorithms do not suffer from simple power analysis (SPA). With the randomization of the secret scalar or base point, three of the four algorithms are secure against differential power analysis (DPA), refined power analysis (RPA) and zero-value point attacks (ZPA). The countermeasures are also immune to the doubling attack. Fast Shamir's method is used in order to improve the efficiency of parallel scalar multiplication. Compared with previous countermeasures, the new countermeasures achieve higher security and do not impact overall performance.
26378993
Dynamic output feedback H/sup /spl infin// controller design of fuzzy dynamic systems using LMI techniques
Presents output feedback H/sup /spl infin// controller design methods for fuzzy dynamic systems. Three kinds of controller design methods are proposed based on a smooth Lyapunov function or a piecewise smooth Lyapunov function. The control laws are obtained by using LMI techniques and the global stability with disturbance attenuation of the closed-loop fuzzy control system is established.
10586032
A tag recommendation system for folksonomy
Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, so that a good tag recommendation system can ease the process of finding some useful and relevant keywords for users. Researchers have made lots of relevant work for tag recommendation systems, but those traditional collaborative systems may not perform well in the case of a new user and a new resource.
 To address this problem, we propose a tag recommendation system for two different cases. The first case is that the active user or active resource is a new one which has not appeared in the past while the second case is that the active user and resource has already been posted. In this paper, we present two different methods for these two cases: a content-based method and a graph-based method. We implement our tag recommendation system in a realworld academic Folksonomy system. In order to gain better results,we apply improvements and combinations of previous work in each of these two methods.and combinations of previous work in each of these two methods. We participated into the Discovery Challenge 2009 with the proposed methods. The results of our teams are within the top teams.
10680010
Automatic sports genre categorization and view-type classification over large-scale dataset
This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications.