14,227 research outputs found
PDFS: Practical Data Feed Service for Smart Contracts
Smart contracts are a new paradigm that emerged with the rise of the
blockchain technology. They allow untrusting parties to arrange agreements.
These agreements are encoded as a programming language code and deployed on a
blockchain platform, where all participants execute them and maintain their
state. Smart contracts are promising since they are automated and
decentralized, thus limiting the involvement of third trusted parties, and can
contain monetary transfers. Due to these features, many people believe that
smart contracts will revolutionize the way we think of distributed
applications, information sharing, financial services, and infrastructures.
To release the potential of smart contracts, it is necessary to connect the
contracts with the outside world, such that they can understand and use
information from other infrastructures. For instance, smart contracts would
greatly benefit when they have access to web content. However, there are many
challenges associated with realizing such a system, and despite the existence
of many proposals, no solution is secure, provides easily-parsable data,
introduces small overheads, and is easy to deploy.
In this paper we propose PDFS, a practical system for data feeds that
combines the advantages of the previous schemes and introduces new
functionalities. PDFS extends content providers by including new features for
data transparency and consistency validations. This combination provides
multiple benefits like content which is easy to parse and efficient
authenticity verification without breaking natural trust chains. PDFS keeps
content providers auditable, mitigates their malicious activities (like data
modification or censorship), and allows them to create a new business model. We
show how PDFS is integrated with existing web services, report on a PDFS
implementation and present results from conducted case studies and experiments.Comment: Blockchain; Smart Contracts; Data Authentication; Ethereu
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
Sustainable Development Report: Blockchain, the Web3 & the SDGs
This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc
Sustainable Development Report: Blockchain, the Web3 & the SDGs
This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc
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