65,092 research outputs found
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Online knowledge repositories typically rely on their users or dedicated
editors to evaluate the reliability of their content. These evaluations can be
viewed as noisy measurements of both information reliability and information
source trustworthiness. Can we leverage these noisy evaluations, often biased,
to distill a robust, unbiased and interpretable measure of both notions?
In this paper, we argue that the temporal traces left by these noisy
evaluations give cues on the reliability of the information and the
trustworthiness of the sources. Then, we propose a temporal point process
modeling framework that links these temporal traces to robust, unbiased and
interpretable notions of information reliability and source trustworthiness.
Furthermore, we develop an efficient convex optimization procedure to learn the
parameters of the model from historical traces. Experiments on real-world data
gathered from Wikipedia and Stack Overflow show that our modeling framework
accurately predicts evaluation events, provides an interpretable measure of
information reliability and source trustworthiness, and yields interesting
insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Outsmarting Network Security with SDN Teleportation
Software-defined networking is considered a promising new paradigm, enabling
more reliable and formally verifiable communication networks. However, this
paper shows that the separation of the control plane from the data plane, which
lies at the heart of Software-Defined Networks (SDNs), introduces a new
vulnerability which we call \emph{teleportation}. An attacker (e.g., a
malicious switch in the data plane or a host connected to the network) can use
teleportation to transmit information via the control plane and bypass critical
network functions in the data plane (e.g., a firewall), and to violate security
policies as well as logical and even physical separations. This paper
characterizes the design space for teleportation attacks theoretically, and
then identifies four different teleportation techniques. We demonstrate and
discuss how these techniques can be exploited for different attacks (e.g.,
exfiltrating confidential data at high rates), and also initiate the discussion
of possible countermeasures. Generally, and given today's trend toward more
intent-based networking, we believe that our findings are relevant beyond the
use cases considered in this paper.Comment: Accepted in EuroSP'1
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