1,325 research outputs found
Beyond the Hype: On Using Blockchains in Trust Management for Authentication
Trust Management (TM) systems for authentication are vital to the security of
online interactions, which are ubiquitous in our everyday lives. Various
systems, like the Web PKI (X.509) and PGP's Web of Trust are used to manage
trust in this setting. In recent years, blockchain technology has been
introduced as a panacea to our security problems, including that of
authentication, without sufficient reasoning, as to its merits.In this work, we
investigate the merits of using open distributed ledgers (ODLs), such as the
one implemented by blockchain technology, for securing TM systems for
authentication. We formally model such systems, and explore how blockchain can
help mitigate attacks against them. After formal argumentation, we conclude
that in the context of Trust Management for authentication, blockchain
technology, and ODLs in general, can offer considerable advantages compared to
previous approaches. Our analysis is, to the best of our knowledge, the first
to formally model and argue about the security of TM systems for
authentication, based on blockchain technology. To achieve this result, we
first provide an abstract model for TM systems for authentication. Then, we
show how this model can be conceptually encoded in a blockchain, by expressing
it as a series of state transitions. As a next step, we examine five prevalent
attacks on TM systems, and provide evidence that blockchain-based solutions can
be beneficial to the security of such systems, by mitigating, or completely
negating such attacks.Comment: A version of this paper was published in IEEE Trustcom.
http://ieeexplore.ieee.org/document/8029486
On the Convergence of Blockchain and Internet of Things (IoT) Technologies
The Internet of Things (IoT) technology will soon become an integral part of
our daily lives to facilitate the control and monitoring of processes and
objects and revolutionize the ways that human interacts with the physical
world. For all features of IoT to become fully functional in practice, there
are several obstacles on the way to be surmounted and critical challenges to be
addressed. These include, but are not limited to cybersecurity, data privacy,
energy consumption, and scalability. The Blockchain decentralized nature and
its multi-faceted procedures offer a useful mechanism to tackle several of
these IoT challenges. However, applying the Blockchain protocols to IoT without
considering their tremendous computational loads, delays, and bandwidth
overhead can let to a new set of problems. This review evaluates some of the
main challenges we face in the integration of Blockchain and IoT technologies
and provides insights and high-level solutions that can potentially handle the
shortcomings and constraints of both IoT and Blockchain technologies.Comment: Includes 11 Pages, 3 Figures, To publish in Journal of Strategic
Innovation and Sustainability for issue JSIS 14(1
RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction
Robots have potential to revolutionize the way we interact with the world
around us. One of their largest potentials is in the domain of mobile health
where they can be used to facilitate clinical interventions. However, to
accomplish this, robots need to have access to our private data in order to
learn from these data and improve their interaction capabilities. Furthermore,
to enhance this learning process, the knowledge sharing among multiple robot
units is the natural step forward. However, to date, there is no
well-established framework which allows for such data sharing while preserving
the privacy of the users (e.g., the hospital patients). To this end, we
introduce RoboChain - the first learning framework for secure, decentralized
and computationally efficient data and model sharing among multiple robot units
installed at multiple sites (e.g., hospitals). RoboChain builds upon and
combines the latest advances in open data access and blockchain technologies,
as well as machine learning. We illustrate this framework using the example of
a clinical intervention conducted in a private network of hospitals.
Specifically, we lay down the system architecture that allows multiple robot
units, conducting the interventions at different hospitals, to perform
efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure
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