Theory and Practice of Cryptography and Network Security Protocols and Technologies

In an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement. When used in coordination with other tools for ensuring information security, cryptography in all of its applications, including data confidentiality, data integrity, and user authentication, is a most powerful tool for protecting information. This book presents a collection of research work in the field of cryptography. It discusses some of the critical challenges that are being faced by the current computing world and also describes some mechanisms to defend against these challenges. It is a valuable source of knowledge for researchers, engineers, graduate and doctoral students working in the field of cryptography. It will also be useful for faculty members of graduate schools and universities

Secure Cryptographic Algorithm Implementation on Embedded Platforms

Sensitive systems that are based on smart cards use well-studied and well-developed cryptosystems. Generally these cryptosystems have been subject to rigorous mathematical analysis in an effort to uncover cryptographic weaknesses in the system. The cryptosystems used in smart cards are, therefore, not usually vulnerable to these types of attacks. Since smart cards are small objects that can be easily placed in an environment where physical vulnerabilities can be exploited, adversaries have turned to different avenues of attack. This thesis describes the current state-of-the-art in side channel and fault analysis against smart cards, and the countermeasures necessary to provide a secure implementation. Both attack techniques need to be taken into consideration when implementing cryptographic algorithms in smart cards. In the domain of side-channel analysis a new application of using cache accesses to attack an implementation of AES by observing the power consumption is described, including an unpublished extension. Several new fault attacks are proposed based on finding collisions between a correct and a fault-induced execution of a secure secret algorithm. Other new fault attacks include reducing the number of rounds of an algorithm to make a differential cryptanalysis trivial, and fixing portions of the random value used in DSA to allow key recovery. Countermeasures are proposed for all the attacks described. The use of random delays, a simple countermeasure, is improved to render it more secure and less costly to implement. Several new countermeasures are proposed to counteract the particular fault attacks proposed in this thesis. A new method of calculating a modular exponentiation that is secure against side channel analysis is described, based on ideas which have been proposed previously or are known within the smart card industry. A novel method for protecting RSA against fault attacks is also proposed based on securing the underlying Montgomery multiplication. The majority of the fault attacks detailed have been implemented against actual chips to demonstrate the feasibility of these attacks. Details of these experiments are given in appendices. The experiments conducted to optimise the performance of random delays are also described in an appendix

Perpetual Sensing: Experiences with Energy-Harvesting Sensor Systems

Industry forecasts project the number of connected devices will outpace the global population by orders of magnitude in the next decade or two. These projections are application driven: smart cities, implantable health monitors, responsive buildings, autonomous robots, driverless cars, and instrumented infrastructure are all expected to be drivers for the growth of networked devices. Achieving this immense scale---potentially trillions of smart and connected sensors and computers, popularly called the "Internet of Things"---raises a host of challenges including operating system design, networking protocols, and orchestration methodologies. However, another critical issue may be the most fundamental: If embedded computers outnumber people by a factor of a thousand, how are we going to keep all of these devices powered? In this dissertation, we show that energy-harvesting operation, by which devices scavenge energy from their surroundings to power themselves after they are deployed, is a viable answer to this question. In particular, we examine a range of energy-harvesting sensor node designs for a specific application: smart buildings. In this application setting, the devices must be small and sleek to be unobtrusively and widely deployed, yet shrinking the devices also reduces their energy budgets as energy storage often dominates their volume. Additionally, energy-harvesting introduces new challenges for these devices due to the intermittent access to power that stems from relying on unpredictable ambient energy sources. To address these challenges, we present several techniques for realizing effective sensors despite the size and energy constraints. First is Monjolo, an energy metering system that exploits rather than attempts to mask the variability in energy-harvesting by using the energy harvester itself as the sensor. Building on Monjolo, we show how simple time synchronization and an application specific sensor can enable accurate, building-scale submetering while remaining energy-harvesting. We also show how energy-harvesting can be the foundation for highly deployable power metering, as well as indoor monitoring and event detection. With these sensors as a guide, we present an architecture for energy-harvesting systems that provides layered abstractions and enables modular component reuse. We also couple these sensors with a generic and reusable gateway platform and an application-layer cloud service to form an easy-to-deploy building sensing toolkit, and demonstrate its effectiveness by performing and analyzing several modest-scale deployments.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138686/1/bradjc_1.pd

Sistemas interativos e distribuídos para telemedicina

doutoramento Ciências da ComputaçãoDurante as últimas décadas, as organizações de saúde têm vindo a adotar continuadamente as tecnologias de informação para melhorar o funcionamento dos seus serviços. Recentemente, em parte devido à crise financeira, algumas reformas no sector de saúde incentivaram o aparecimento de novas soluções de telemedicina para otimizar a utilização de recursos humanos e de equipamentos. Algumas tecnologias como a computação em nuvem, a computação móvel e os sistemas Web, têm sido importantes para o sucesso destas novas aplicações de telemedicina. As funcionalidades emergentes de computação distribuída facilitam a ligação de comunidades médicas, promovem serviços de telemedicina e a colaboração em tempo real. Também são evidentes algumas vantagens que os dispositivos móveis podem introduzir, tais como facilitar o trabalho remoto a qualquer hora e em qualquer lugar. Por outro lado, muitas funcionalidades que se tornaram comuns nas redes sociais, tais como a partilha de dados, a troca de mensagens, os fóruns de discussão e a videoconferência, têm o potencial para promover a colaboração no sector da saúde. Esta tese teve como objetivo principal investigar soluções computacionais mais ágeis que permitam promover a partilha de dados clínicos e facilitar a criação de fluxos de trabalho colaborativos em radiologia. Através da exploração das atuais tecnologias Web e de computação móvel, concebemos uma solução ubíqua para a visualização de imagens médicas e desenvolvemos um sistema colaborativo para a área de radiologia, baseado na tecnologia da computação em nuvem. Neste percurso, foram investigadas metodologias de mineração de texto, de representação semântica e de recuperação de informação baseada no conteúdo da imagem. Para garantir a privacidade dos pacientes e agilizar o processo de partilha de dados em ambientes colaborativos, propomos ainda uma metodologia que usa aprendizagem automática para anonimizar as imagens médicasDuring the last decades, healthcare organizations have been increasingly relying on information technologies to improve their services. At the same time, the optimization of resources, both professionals and equipment, have promoted the emergence of telemedicine solutions. Some technologies including cloud computing, mobile computing, web systems and distributed computing can be used to facilitate the creation of medical communities, and the promotion of telemedicine services and real-time collaboration. On the other hand, many features that have become commonplace in social networks, such as data sharing, message exchange, discussion forums, and a videoconference, have also the potential to foster collaboration in the health sector. The main objective of this research work was to investigate computational solutions that allow us to promote the sharing of clinical data and to facilitate the creation of collaborative workflows in radiology. By exploring computing and mobile computing technologies, we have designed a solution for medical imaging visualization, and developed a collaborative system for radiology, based on cloud computing technology. To extract more information from data, we investigated several methodologies such as text mining, semantic representation, content-based information retrieval. Finally, to ensure patient privacy and to streamline the data sharing in collaborative environments, we propose a machine learning methodology to anonymize medical images

EVA London 2022: Electronic Visualisation and the Arts

The Electronic Visualisation and the Arts London 2022 Conference (EVA London 2022) is co-sponsored by the Computer Arts Society (CAS) and BCS, the Chartered Institute for IT, of which the CAS is a Specialist Group. Of course, this has been a difficult time for all conferences, with the Covid-19 pandemic. For the first time since 2019, the EVA London 2022 Conference is a physical conference. It is also an online conference, as it was in the previous two years. We continue with publishing the proceedings, both online, with open access via ScienceOpen, and also in our traditional printed form, for the second year in full colour. Over recent decades, the EVA London Conference on Electronic Visualisation and the Arts has established itself as one of the United Kingdom’s most innovative and interdisciplinary conferences. It brings together a wide range of research domains to celebrate a diverse set of interests, with a specialised focus on visualisation. The long and short papers in this volume cover varied topics concerning the arts, visualisations, and IT, including 3D graphics, animation, artificial intelligence, creativity, culture, design, digital art, ethics, heritage, literature, museums, music, philosophy, politics, publishing, social media, and virtual reality, as well as other related interdisciplinary areas. The EVA London 2022 proceedings presents a wide spectrum of papers, demonstrations, Research Workshop contributions, other workshops, and for the seventh year, the EVA London Symposium, in the form of an opening morning session, with three invited contributors. The conference includes a number of other associated evening events including ones organised by the Computer Arts Society, Art in Flux, and EVA International. As in previous years, there are Research Workshop contributions in this volume, aimed at encouraging participation by postgraduate students and early-career artists, accepted either through the peer-review process or directly by the Research Workshop chair. The Research Workshop contributors are offered bursaries to aid participation. In particular, EVA London liaises with Art in Flux, a London-based group of digital artists. The EVA London 2022 proceedings includes long papers and short “poster” papers from international researchers inside and outside academia, from graduate artists, PhD students, industry professionals, established scholars, and senior researchers, who value EVA London for its interdisciplinary community. The conference also features keynote talks. A special feature this year is support for Ukrainian culture after its invasion earlier in the year. This publication has resulted from a selective peer review process, fitting as many excellent submissions as possible into the proceedings. This year, submission numbers were lower than previous years, mostly likely due to the pandemic and a new requirement to submit drafts of long papers for review as well as abstracts. It is still pleasing to have so many good proposals from which to select the papers that have been included. EVA London is part of a larger network of EVA international conferences. EVA events have been held in Athens, Beijing, Berlin, Brussels, California, Cambridge (both UK and USA), Canberra, Copenhagen, Dallas, Delhi, Edinburgh, Florence, Gifu (Japan), Glasgow, Harvard, Jerusalem, Kiev, Laval, London, Madrid, Montreal, Moscow, New York, Paris, Prague, St Petersburg, Thessaloniki, and Warsaw. Further venues for EVA conferences are very much encouraged by the EVA community. As noted earlier, this volume is a record of accepted submissions to EVA London 2022. Associated online presentations are in general recorded and made available online after the conference

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research