29,786 research outputs found
A flexible architecture for privacy-aware trust management
In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.\u
A Multi-perspective Analysis of Carrier-Grade NAT Deployment
As ISPs face IPv4 address scarcity they increasingly turn to network address
translation (NAT) to accommodate the address needs of their customers.
Recently, ISPs have moved beyond employing NATs only directly at individual
customers and instead begun deploying Carrier-Grade NATs (CGNs) to apply
address translation to many independent and disparate endpoints spanning
physical locations, a phenomenon that so far has received little in the way of
empirical assessment. In this work we present a broad and systematic study of
the deployment and behavior of these middleboxes. We develop a methodology to
detect the existence of hosts behind CGNs by extracting non-routable IP
addresses from peer lists we obtain by crawling the BitTorrent DHT. We
complement this approach with improvements to our Netalyzr troubleshooting
service, enabling us to determine a range of indicators of CGN presence as well
as detailed insights into key properties of CGNs. Combining the two data
sources we illustrate the scope of CGN deployment on today's Internet, and
report on characteristics of commonly deployed CGNs and their effect on end
users
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
On Factors Affecting the Usage and Adoption of a Nation-wide TV Streaming Service
Using nine months of access logs comprising 1.9 Billion sessions to BBC
iPlayer, we survey the UK ISP ecosystem to understand the factors affecting
adoption and usage of a high bandwidth TV streaming application across
different providers. We find evidence that connection speeds are important and
that external events can have a huge impact for live TV usage. Then, through a
temporal analysis of the access logs, we demonstrate that data usage caps
imposed by mobile ISPs significantly affect usage patterns, and look for
solutions. We show that product bundle discounts with a related fixed-line ISP,
a strategy already employed by some mobile providers, can better support user
needs and capture a bigger share of accesses. We observe that users regularly
split their sessions between mobile and fixed-line connections, suggesting a
straightforward strategy for offloading by speculatively pre-fetching content
from a fixed-line ISP before access on mobile devices.Comment: In Proceedings of IEEE INFOCOM 201
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