Quantum error correction with biased noise

Quantum computing offers powerful new techniques for speeding up the calculation of many classically intractable problems. Quantum algorithms can allow for the efficient simulation of physical systems, with applications to basic research, chemical modeling, and drug discovery; other algorithms have important implications for cryptography and internet security.

At the same time, building a quantum computer is a daunting task, requiring the coherent manipulation of systems with many quantum degrees of freedom while preventing environmental noise from interacting too strongly with the system. Fortunately, we know that, under reasonable assumptions, we can use the techniques of quantum error correction and fault tolerance to achieve an arbitrary reduction in the noise level.

In this thesis, we look at how additional information about the structure of noise, or "noise bias," can improve or alter the performance of techniques in quantum error correction and fault tolerance. In Chapter 2, we explore the possibility of designing certain quantum gates to be extremely robust with respect to errors in their operation. This naturally leads to structured noise where certain gates can be implemented in a protected manner, allowing the user to focus their protection on the noisier unprotected operations.

In Chapter 3, we examine how to tailor error-correcting codes and fault-tolerant quantum circuits in the presence of dephasing biased noise, where dephasing errors are far more common than bit-flip errors. By using an appropriately asymmetric code, we demonstrate the ability to improve the amount of error reduction and decrease the physical resources required for error correction.

In Chapter 4, we analyze a variety of protocols for distilling magic states, which enable universal quantum computation, in the presence of faulty Clifford operations. Here again there is a hierarchy of noise levels, with a fixed error rate for faulty gates, and a second rate for errors in the distilled states which decreases as the states are distilled to better quality. The interplay of of these different rates sets limits on the achievable distillation and how quickly states converge to that limit.

Accelerating Internet Streaming Media Delivery Using Network-Aware Partial Caching

Internet streaming applications are adversely affected by network conditions such as high packet loss rates and long delays. This paper aims at mitigating such effects by leveraging the availability of client-side caching proxies. We present a novel caching architecture (and associated cache management algorithms) that turn edge caches into accelerators of streaming media delivery. A salient feature of our caching algorithms is that they allow partial caching of streaming media objects and joint delivery of content from caches and origin servers. The caching algorithms we propose are both network-aware and stream-aware; they take into account the popularity of streaming media objects, their bit-rate requirements, and the available bandwidth between clients and servers. Using realistic models of Internet bandwidth (derived from proxy cache logs and measured over real Internet paths), we have conducted extensive simulations to evaluate the performance of various cache management alternatives. Our experiments demonstrate that network-aware caching algorithms can significantly reduce service delay and improve overall stream quality. Also, our experiments show that partial caching is particularly effective when bandwidth variability is not very high.

Worldwide Infrastructure Security Report 2011

Arbor Network's annual Internet security report for 2011/12. We will discuss this report in INFO6003 lectures.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Crocodiles in the regulatory swamp: navigating the dangers of outsourcing, SaaS and shadow IT

Corporates are entering the brave new world of the internet and digitization without much regard for the fine print of a growing regulation regime. More traditional outsourcing arrangements are already falling foul of the regulators as rules and supervision intensifies. Furthermore, ‘shadow IT’ is proliferating as the attractions of SaaS, mobile, cloud services, social media, and endless new ‘apps’ drive usage outside corporate IT. Initial cost-benefit analyses of the Cloud make such arrangements look immediately attractive but losing control of architecture, security, applications and deployment can have far reaching and damaging regulatory consequences. From research in financial services, this paper details the increasing body of regulations, their inherent risks for businesses and how the dangers can be pre-empted and managed. We then delineate a model for managing these risks specifically focused on investigating, strategizing and governing outsourcing arrangements and related regulatory obligations

A security evaluation criteria for baseline security standards

Recent advances in technology and new software applications are steadily transforming human civilization into what is called the Information Society. This is manifested by the new terminology appearing in our daily activities. E-Business, E-Government, E-Learning, E-Contracting, and E-Voting are just a few of the ever-growing list of new terms that are shaping the Information Society. Nonetheless, as "Information" gains more prominence in our society, the task of securing it against all forms of threats becomes a vital and crucial undertaking. Addressing the various security issues confronting our new Information Society, this volume is divided into 13 parts covering the following topics: Information Security Management; Standards of Information Security; Threats and Attacks to Information; Education and Curriculum for Information Security; Social and Ethical Aspects of Information Security; Information Security Services; Multilateral Security; Applications of Information Security; Infrastructure for Information Security Advanced Topics in Security; Legislation for Information Security; Modeling and Analysis for Information Security; Tools for Information Security. Security in the Information Society: Visions and Perspectives comprises the proceedings of the 17th International Conference on Information Security (SEC2002), which was sponsored by the International Federation for Information Processing (IFIP), and jointly organized by IFIP Technical Committee 11 and the Department of Electronics and Electrical Communications of Cairo University. The conference was held in May 2002 in Cairo, Egypt. This volume is essential reading for scholars, researchers, and practitioners interested inkeeping pace with the ever-growing field of Information Security.

The road to internet banking: understanding emerging customer issues

This paper presents findings from an empirical study of banking customer experiences with the adoption of Internet banking. Using a qualitative, interpretive grounded theory approach and single and group interviews, the study explores customer perceptions and experiences and provides an understanding of how and why specific factors affect their decision whether or not to bank on the Internet in the current era. The findings are used to develop a theoretical framework which conceptualizes and links consumer issues influencing the adoption of this application, and we also provide a set of recommendations for banks. Specifically, the findings suggest that convenience – in particular, time savings – is the major motivator to bank on the Internet, while there are a range of other influential factors which could be modulated by banks. The results also highlight increasing online risk acceptance by customers and the growing importance of deep levels of customer support for online services. Key gender differences in attitudes to Internet banking are highlighted. This study suggests that organizations will improve their management of customer attitudes to new Internet service applications by understanding the need to proactively address customer fears and misconceptions about the technologies involved.

The Australian small to medium enterprise E-business Security Methodology (ASME-EBSM)

This research investigated Australian SMEs (small to medium enterprises), E-business and strategies for security management. Limitations of current approaches and empirical survey results produced the Australian SME E-business Security Methodology. This new approach delivers recommendations to provide e-business security management strategies for micro, small and medium SME e-business systems.

Management framework for corporate telecommunication and data centre information security

Network security, particularly Internet security, is at the forefront of business and government networks. This research has discovered weaknesses in current professional practice, particularly in mitigation strategies to reduce the impacts of security violations in corporate telecommunications and data centres. The importance of integrating security policies, processes and operational practice is demonstrated. Leadership models and innovation mechanisms best suited to improved security design are also identified.

Detecting stepping stones in internet environments

An effective and efficient scheme along with a number of related algorithms were presented in this thesis for detecting stepping stones in real Internet environments, even when evasion techniques are used by attackers. A high quantitative comparative experimental analysis on stepping stone detection systems was conducted as well.

Classifying network traffic in the big data era

With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.

A competitive three-level pruning technique for information security

The reduction of size of ensemble classifiers is important for various security applications. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization based methods. The present paper introduces and investigates a new pruning technique. It is called a Three-Level Pruning Technique, TLPT, because it simultaneously combines all three approaches in three levels of the process. This paper investigates the TLPT method combining the state-of-the-art ranking of the Ensemble Pruning via Individual Contribution ordering, EPIC, the clustering of the K-Means Pruning, KMP, and the optimisation method of Directed Hill Climbing Ensemble Pruning, DHCEP, for a phishing dataset. Our new experiments presented in this paper show that the TLPT is competitive in comparison to EPIC, KMP and DHCEP, and can achieve better outcomes. These experimental results demonstrate the effectiveness of the TLPT technique in this example of information security application.

Segurança na internet: a percepção do usuário como fator de restrição ao crescimento do comércio eletrônico no Brasil

O comércio eletrônico já é uma realidade brasileira. Contudo, esta modalidade de negócio eletrônico ainda não atingiu o seu pleno potencial, especialmente nas negociações orientadas para o consumidor (B2C). Vários fatores são apontados como restrições ao seu crescimento, mas nenhum deles é tão destacado e controvertido quanto a segurança na Internet, especialmente nas transações eletrônicas. Este trabalho analisa a questão da segurança do ponto de vista dos usuários de Internet, uma vez que a percepção de segurança dos internautas determina a sua confiança, e a sua confiança influencia a sua decisão de compra eletrônica e a abrangência das compras realizadas através da Internet. A segurança, vista freqüentemente como a grande vilã no mundo digital, passa a ser entendida alternativamente como um dos fundamentos do comércio eletrônico e, conseqüentemente, uma grande vantagem competitiva para os negócios eletrônicos.

The effect of social security, health, demography and technology on retirement

This article studies the determinants of the labor force participation of the elderly and investigates the factors that may account for the increase in retirement in the second half of the last century. We develop a life-cycle general equilibrium model with endogenous retirement that embeds Social Security legislation and Medicare. Individuals are ex ante heterogeneous with respect to their preferences for leisure and face uncertainty about labor productivity, health status and out-of-pocket medical expenses. The model is calibrated to the U.S. economy in 2000 and is able to reproduce very closely the retirement behavior of the American population. It reproduces the peaks in the distribution of Social Security applications at ages 62 and 65 and the observed facts that low earners and unhealthy individuals retire earlier. It also matches very closely the increase in retirement from 1950 to 2000. Changes in Social Security policy - which became much more generous - and the introduction of Medicare account for most of the expansion of retirement. In contrast, the isolated impact of the increase in longevity was a delaying of retirement.