The enablers and implementation model for mobile KMS in Australian healthcare

In this research project, the enablers in implementing mobile KMS in Australian regional healthcare will be investigated, and a validated framework and guidelines to assist healthcare in implementing mobile KMS will also be proposed with both qualitative and quantitative approaches. The outcomes for this study are expected to improve the understanding the enabling factors in implementing mobile KMS in Australian healthcare, as well as provide better guidelines for this process

Optimization and Applications of Modern Wireless Networks and Symmetry

Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Code-based Identification and Signature Schemes

In an age of explosive growth of digital communications and electronic data storage, cryptography plays an integral role in our society. Some examples of daily use of cryptography are software updates, e-banking, electronic commerce, ATM cards, etc. The security of most currently used cryptosystems relies on the hardness of the factorization and discrete logarithm problems. However, in 1994 Peter Shor discovered polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. Therefore, it is of extreme importance to develop cryptosystems that remain secure even when the adversary has access to a quantum computer; such systems are called post-quantum cryptosystems. One promising candidate is based on codes; in this thesis we focus more specifically on code-based identification and signature schemes. Public key identification schemes are typically applied in cryptography to reach the goal of entity authentication. Their applications include authentication and access control services such as remote login, credit card purchases and many others. One of the most well-known systems of this kind is the zero-knowledge identification scheme introduced in Crypto 1993 by Stern. It is very fast compared to schemes based on number-theoretic problems since it involves only simple and efficiently executable operations. However, its main drawbacks are the high communication complexity and the large public key size, that makes it impractical for many applications. Our first contribution addresses these drawbacks by taking a step towards reducing communication complexity and public key size simultaneously. To this end, we propose a novel zero-knowledge five-pass identification scheme which improves on Stern's scheme. It reduces the communication complexity by a factor of 25 % compared to Stern's one. Moreover, we obtain a public key of size of 4 KB, whereas Stern's scheme requires 15 KB for the same level of security. To the best of our knowledge, there is no code-based identification scheme with better performance than our proposal using random codes. Our second contribution consists of extending one of the most important paradigms in cryptography, namely the one by Fiat and Shamir. In doing so, we enlarge the class of identification schemes to which the Fiat-Shamir transform can be applied. Additionally, we put forward a generic methodology for proving the security of signature schemes derived from this class of identification schemes. We exemplify our extended paradigm and derive a provably secure signature scheme based on our proposed five-pass identification scheme. In order to contribute to the development of post-quantum schemes with additional features, we present an improved code-based threshold ring signature scheme using our two previous results. Our proposal has a shorter signature length and a smaller public-key size compared to Aguilar et al.'s scheme, which is the reference in this area

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author