Protection against spam, fraud and phishing becomes increasingly important in the applications of social networks. Online social network providers such as Facebook and MySpace collect data from users including their relation and education statuses. While these data are used to provide users with convenient services, improper use of these data such as spam advertisement can be annoying and even harmful. Even worse, if these data are somehow stolen or illegally gathered, the users might be exposed to fraud and phishing. To further protect individual privacy, we employ an intimacy algorithm without the violation of privacy. Also, we explore spammers through detecting unusual intimacy phenomenon. We, therefore, propose a mathematical model for intimacy-based security protection in a social network without the violation of privacy in this paper. Moreover, the feasibility and the effectiveness of our model is testified theoretically and experimentally.

The construction of a graph is extremely important in graph-based semi-supervised learning. However, it is unstable by virtue of sensitivity to the selection of neighbourhood parameter and inaccuracy of the edge weights. Inspired by the good performance of the local learning method, this paper proposes a semi-supervised dimensionality reduction based on local estimation error (LEESSDR) algorithm by utilising local learning projections (LLP) to semi-supervised dimensionality reduction. The algorithm sets the edge weights through minimising the local estimation error and can effectively preserve the global geometric structure as well as the local one of the data. Since LLP does not require its input space to be locally linear, even if it is nonlinear, LLP maps it to the feature space by using kernel functions and then obtains its local estimation error in the feature space. The effectiveness of the proposed method is verified on two popular face databases with promising classification accuracy and favourable robustness.

In cloud storage, the most common concerns for users are data integrity, confidentiality and availability. Recently, many data integrity auditing schemes have been proposed, some of which achieved privacy-preserving public auditing, some of which realised data sharing and group dynamic, and some of which supported data dynamic. However, as far as we know, there does not exist a practical auditing scheme which can simultaneously realise all the functions mentioned above; In addition, all the existing schemes adopt the method of computing message authentication code (MAC) by users to achieve the following data integrity auditing, nevertheless, it is a arduous task to compute MACs for these cloud users with constrained computing and storage capabilities. In this paper, we propose a novel privacy-preserving public auditing scheme for shared data in the cloud, which can also support data dynamic operation and group dynamic. First, we bring proxy signature into the existing auditing scheme. The cloud user can delegate the computation of MACs to a cloud computing server, so that the user's burden would be greatly reduced; second, by introducing a Lagrange interpolating polynomial, our scheme realises the identity privacy-preserving in the precondition of almost no addition of any new computation and less storage space, moreover it makes group dynamic simple; third, we make an index-switch table and combine it with the Merkle hash tree to realise the practical and secure dynamic operations of shared data by group users; fourth, in our scheme, the cloud storage server will add its private key in producing the proof information to protect the data privacy and resist the active attack. Theoretical analysis demonstrates our scheme is provably secure.

Exposing opportunities for parallelisation while explicitly managing data locality is the primary challenge to porting and optimising computational science simulation codes to improve performance. ...

Current trends in computer architecture are increasingly moving towards heterogeneous platforms, now including FPGAs as first-class components enabling unprecedented levels of performance and power-efficiency. Programming such next-generation machines is however extremely difficult as it requires architecture-specific code and low-level hardware design. This paper describes the main outcomes of the HtComp project, a two-year research programme aimed at exploring methodologies and tools allowing the automated generation of FPGA-based accelerators from high-level applications written in traditional software languages. In particular, the paper describes the main contributions brought by the project, covering the generation of hardware systems from high-level parallel code, the performance-oriented optimisation of memory architectures tailored on the application access patterns, as well as the automated definition of application-driven special-purpose on-chip interconnects. Overall, the above innovations contributed to creating a viable path allowing generic software developers to access tomorrow's hardware-accelerated high-performance platforms with minimum development overheads.

Ambient intelligence (AmI) is a new concept that interests many researchers in different domains that require highly innovative services, such as vehicular ad hoc networks (VANETs). In fact, concerning vehicle trust management, the majority of the proposed solutions are dependent on road equipments and the investigation into a scalable infrastructure in all the roads is unreasonable. Therefore, road security should be managed by vehicles themselves. Hence, we have introduced the concept of 'active vehicle' that combines the power of AmI and the V2V technologies. In this paper, we propose a new secured communication model between active vehicles for alert spreading. Through this new model, traffic accident alerts are verified depending on the trust level of the sender. A trust management system has the responsibility to cut out the spread of false warnings through the network.

The increasing life expectations and low birth rates radically changed the demographic structure of the European Union. The number of elderly people is growing up as well as the number of chronic diseases: this implies higher and higher healthcare costs, a reduction of healthcare personnel and requests for better care services. E-health has led to a growth of the health organisation, providing innovative and not-intrusive systems together with value-added services to the healthcare actors that contribute to enhance the efficiency and reduce the costs of complex informative systems. Many research efforts lead to innovative and not-intrusive e-health systems. In this work, we present a system for supporting medical decisions. It is based on semantic analysis of available medical data. The system implements an innovative methodology, which combines different semantic approaches in order to extract the representation of a given document expressed in natural language, and to associate it to a set of RDF triples.

Featuring no key escrow and no certificate, certificateless public key cryptography (CLPKC) has received much attention. A necessary problem in CLPKC is how to revoke a compromised/misbehaving user, which still remains to be efficiently solved: all existing methods are not practical for use owing to either enormous computation (secret channels), or a costly mediator. In this paper, we present a new approach for revocation in CLPKC and give a revocable certificateless encryption (RCLE) scheme. In the new scheme, a user's private key contains three parts: an initial partial private key, a time key and a secret value. The time key is updated at every time period and is transmitted over a public channel. Our construction offers higher performance than previous solutions. Based on the bilinear Diffie-Hellman problem, our RCLE scheme is provably secure in the random oracle model.

Ontology mapping is quite a useful solution for matching semantics between ontologies or schemas that were designed independently of each other. The traditional ontology is mainly to describe concepts and hierarchy relations between concepts, which easily cause 'tennis problem' and impact the accuracy of concept mapping, especially while integrating data in event-centred domains. In this paper, we give the definition of event ontology mapping, and then propose a semantic similarity calculation model for event ontology mapping, which enable the mapping between event-based information with more abundant semantics, and especially enable the semantic mapping between event elements in different event classes. Experiments show that the proposed model can effectively identify the semantic relations of event classes in two event ontologies.

Cloud federation is an emerging computing model where multiple resources from independent cloud providers are leveraged to create large-scale distributed virtual computing clusters, operating as within a single cloud organisation. This concept of service aggregation is characterised by interoperability features, which can address different problems about inter-cloud collaboration. One of the main issues within a cloud federation is related to the monitoring of the application deployed on resources coming from different vendors belonging to the federation. We present an agent-based architecture that aims at monitoring the user's cloud environment provided by the federation: the elasticity of the proposed architecture allows the configuration and customisation of the monitoring infrastructure to adapt it to the specific cloud application. Agents will be strengthened by algorithms and rules used to monitor QoS parameters that are critical for the specific application. The prototypal implementation of the proposed framework will be applied on a testbed application to validate the monitoring architecture.