42,601 research outputs found
Multiple imputation for sharing precise geographies in public use data
When releasing data to the public, data stewards are ethically and often
legally obligated to protect the confidentiality of data subjects' identities
and sensitive attributes. They also strive to release data that are informative
for a wide range of secondary analyses. Achieving both objectives is
particularly challenging when data stewards seek to release highly resolved
geographical information. We present an approach for protecting the
confidentiality of data with geographic identifiers based on multiple
imputation. The basic idea is to convert geography to latitude and longitude,
estimate a bivariate response model conditional on attributes, and simulate new
latitude and longitude values from these models. We illustrate the proposed
methods using data describing causes of death in Durham, North Carolina. In the
context of the application, we present a straightforward tool for generating
simulated geographies and attributes based on regression trees, and we present
methods for assessing disclosure risks with such simulated data.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS506 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Multitask Learning for Network Traffic Classification
Traffic classification has various applications in today's Internet, from
resource allocation, billing and QoS purposes in ISPs to firewall and malware
detection in clients. Classical machine learning algorithms and deep learning
models have been widely used to solve the traffic classification task. However,
training such models requires a large amount of labeled data. Labeling data is
often the most difficult and time-consuming process in building a classifier.
To solve this challenge, we reformulate the traffic classification into a
multi-task learning framework where bandwidth requirement and duration of a
flow are predicted along with the traffic class. The motivation of this
approach is twofold: First, bandwidth requirement and duration are useful in
many applications, including routing, resource allocation, and QoS
provisioning. Second, these two values can be obtained from each flow easily
without the need for human labeling or capturing flows in a controlled and
isolated environment. We show that with a large amount of easily obtainable
data samples for bandwidth and duration prediction tasks, and only a few data
samples for the traffic classification task, one can achieve high accuracy. We
conduct two experiment with ISCX and QUIC public datasets and show the efficacy
of our approach
Internet-based 'social sharing' as a new form of global production: The case of SETI@home
Benkler ('Sharing Nicely', Yale Law Journal, 2004, Vol. 114, pp. 273-358) has argued that 'social sharing' via Internet-based distributed computing is a new, so far under-appreciated modality of economic production. This paper presents results from an empirical study of SETI@home (the Search for Extraterrestrial Intelligence), which is the classic example of such a computing project. The aim is to explain SETI@home participation and its intensity in a cross-country setting. The data are for a sample of 172 developed and developing countries for the years 2002-2004. The results indicate that SETI@home participation and its intensity can be explained largely by the degree of ICT access (proxied by the International Telecommunication Union's 'Digital Access Index'), as well as GDP per capita and dummy variables for major country groups. Some other variables, such as the Human Development Index, perform less well. Although SETI@home is a global phenomenon, it is never-the-less mostly concentrated in rich countries. However, there are indications of a slowly narrowing global SETI@home digital divide
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