Wireless ad hoc network is one of the most suitable platforms for providing communication services to support mobile applications in public areas where no fixed communication infrastructure exists. However, due to the open nature of wireless links and lack of security infrastructure in an ad hoc network environment, applications operating on ad hoc network platforms are subjected to non-trivial security challenges. Asymmetric key management, which is widely adopted to be an effective basis for security services in an open network environment, typically plays a crucial role in meeting the security requirements of such applications. In this paper, we propose a secure asymmetric key management scheme, the Ubiquitous and Secure Certificate Service (USCS), which is based on a variant of the Distributed Certificate Authority (DCA) - the Fully Distributed Certificate Authority (FDCA). Similar to FDCA, USCS introduces the presence of 1-hop neighbors which hold shares of DCA's private signature key, and can collaborate to issue certificates, thereby providing asymmetric key management service. Both USCS and FDCA aim to achieve higher availability than the basic DCA scheme; however, USCS is more secure than FDCA in that the former achieves high availability by distributing existing shares to new members, rather than generating new shares as the FDCA scheme does. In order to realise the high availability potential of USCS, a share selection algorithm is also proposed. Experimental results demonstrated that USCS is a more secure approach of the DCA scheme in that it can achieve stronger security than FDCA while attaining high availability similar to that of FDCA. Experiments also showed that USCS incurs only moderate communication overheads.

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e.g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.