2,236 research outputs found
Energy efficient mining on a quantum-enabled blockchain using light
We outline a quantum-enabled blockchain architecture based on a consortium of
quantum servers. The network is hybridised, utilising digital systems for
sharing and processing classical information combined with a fibre--optic
infrastructure and quantum devices for transmitting and processing quantum
information. We deliver an energy efficient interactive mining protocol enacted
between clients and servers which uses quantum information encoded in light and
removes the need for trust in network infrastructure. Instead, clients on the
network need only trust the transparent network code, and that their devices
adhere to the rules of quantum physics. To demonstrate the energy efficiency of
the mining protocol, we elaborate upon the results of two previous experiments
(one performed over 1km of optical fibre) as applied to this work. Finally, we
address some key vulnerabilities, explore open questions, and observe
forward--compatibility with the quantum internet and quantum computing
technologies.Comment: 25 pages, 5 figure
OSCAR: A Collaborative Bandwidth Aggregation System
The exponential increase in mobile data demand, coupled with growing user
expectation to be connected in all places at all times, have introduced novel
challenges for researchers to address. Fortunately, the wide spread deployment
of various network technologies and the increased adoption of multi-interface
enabled devices have enabled researchers to develop solutions for those
challenges. Such solutions aim to exploit available interfaces on such devices
in both solitary and collaborative forms. These solutions, however, have faced
a steep deployment barrier.
In this paper, we present OSCAR, a multi-objective, incentive-based,
collaborative, and deployable bandwidth aggregation system. We present the
OSCAR architecture that does not introduce any intermediate hardware nor
require changes to current applications or legacy servers. The OSCAR
architecture is designed to automatically estimate the system's context,
dynamically schedule various connections and/or packets to different
interfaces, be backwards compatible with the current Internet architecture, and
provide the user with incentives for collaboration. We also formulate the OSCAR
scheduler as a multi-objective, multi-modal scheduler that maximizes system
throughput while minimizing energy consumption or financial cost. We evaluate
OSCAR via implementation on Linux, as well as via simulation, and compare our
results to the current optimal achievable throughput, cost, and energy
consumption. Our evaluation shows that, in the throughput maximization mode, we
provide up to 150% enhancement in throughput compared to current operating
systems, without any changes to legacy servers. Moreover, this performance gain
further increases with the availability of connection resume-supporting, or
OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput
Dynamic Multiparty Authentication of Data Analytics Services within Cloud Environments
Business analytics processes are often composed from orchestrated,
collaborating services, which are consumed by users from multiple cloud systems
(in different security realms), which need to be engaged dynamically at
runtime. If heterogeneous cloud systems located in different security realms do
not have direct authentication relationships, then it is a considerable
technical challenge to enable secure collaboration. In order to address this
security challenge, a new authentication framework is required to establish
trust amongst business analytics service instances and users by distributing a
common session secret to all participants of a session. We address this
challenge by designing and implementing a secure multiparty authentication
framework for dynamic interaction, for the scenario where members of different
security realms express a need to access orchestrated services. This novel
framework exploits the relationship of trust between session members in
different security realms, to enable a user to obtain security credentials that
access cloud resources in a remote realm. The mechanism assists cloud session
users to authenticate their session membership, thereby improving the
performance of authentication processes within multiparty sessions. We see
applicability of this framework beyond multiple cloud infrastructure, to that
of any scenario where multiple security realms has the potential to exist, such
as the emerging Internet of Things (IoT).Comment: Submitted to the 20th IEEE International Conference on High
Performance Computing and Communications 2018 (HPCC2018), 28-30 June 2018,
Exeter, U
E-infrastructures fostering multi-centre collaborative research into the intensive care management of patients with brain injury
Clinical research is becoming ever more collaborative with multi-centre trials now a common practice. With this in mind, never has it been more important to have secure access to data and, in so doing, tackle the challenges of inter-organisational data access and usage. This is especially the case for research conducted within the brain injury domain due to the complicated multi-trauma nature of the disease with its associated complex collation of time-series data of varying resolution and quality. It is now widely accepted that advances in treatment within this group of patients will only be delivered if the technical infrastructures underpinning the collection and validation of multi-centre research data for clinical trials is improved. In recognition of this need, IT-based multi-centre e-Infrastructures such as the Brain Monitoring with Information Technology group (BrainIT - www.brainit.org) and Cooperative Study on Brain Injury Depolarisations (COSBID - www.cosbid.de) have been formed. A serious impediment to the effective implementation of these networks is access to the know-how and experience needed to install, deploy and manage security-oriented middleware systems that provide secure access to distributed hospital based datasets and especially the linkage of these data sets across sites. The recently funded EU framework VII ICT project Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network (AVERT-IT) is focused upon tackling these challenges. This chapter describes the problems inherent to data collection within the brain injury medical domain, the current IT-based solutions designed to address these problems and how they perform in practice. We outline how the authors have collaborated towards developing Grid solutions to address the major technical issues. Towards this end we describe a prototype solution which ultimately formed the basis for the AVERT-IT project. We describe the design of the underlying Grid infrastructure for AVERT-IT and how it will be used to produce novel approaches to data collection, data validation and clinical trial design is also presented
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