678 research outputs found
ARPA Whitepaper
We propose a secure computation solution for blockchain networks. The
correctness of computation is verifiable even under malicious majority
condition using information-theoretic Message Authentication Code (MAC), and
the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty
computation protocol and a layer2 solution, our privacy-preserving computation
guarantees data security on blockchain, cryptographically, while reducing the
heavy-lifting computation job to a few nodes. This breakthrough has several
implications on the future of decentralized networks. First, secure computation
can be used to support Private Smart Contracts, where consensus is reached
without exposing the information in the public contract. Second, it enables
data to be shared and used in trustless network, without disclosing the raw
data during data-at-use, where data ownership and data usage is safely
separated. Last but not least, computation and verification processes are
separated, which can be perceived as computational sharding, this effectively
makes the transaction processing speed linear to the number of participating
nodes. Our objective is to deploy our secure computation network as an layer2
solution to any blockchain system. Smart Contracts\cite{smartcontract} will be
used as bridge to link the blockchain and computation networks. Additionally,
they will be used as verifier to ensure that outsourced computation is
completed correctly. In order to achieve this, we first develop a general MPC
network with advanced features, such as: 1) Secure Computation, 2) Off-chain
Computation, 3) Verifiable Computation, and 4)Support dApps' needs like
privacy-preserving data exchange
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
The most essential component of every Distributed Ledger Technology (DLT) is
the Consensus Algorithm (CA), which enables users to reach a consensus in a
decentralized and distributed manner. Numerous CA exist, but their viability
for particular applications varies, making their trade-offs a crucial factor to
consider when implementing DLT in a specific field. This article provided a
comprehensive analysis of the various consensus algorithms used in distributed
ledger technologies (DLT) and blockchain networks. We cover an extensive array
of thirty consensus algorithms. Eleven attributes including hardware
requirements, pre-trust level, tolerance level, and more, were used to generate
a series of comparison tables evaluating these consensus algorithms. In
addition, we discuss DLT classifications, the categories of certain consensus
algorithms, and provide examples of authentication-focused and
data-storage-focused DLTs. In addition, we analyze the pros and cons of
particular consensus algorithms, such as Nominated Proof of Stake (NPoS),
Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the
applicability of these consensus algorithms to various Cyber Physical System
(CPS) use cases, including supply chain management, intelligent transportation
systems, and smart healthcare.Comment: 50 pages, 20 figure
A PoW-less Bitcoin with Certified Byzantine Consensus
Distributed Ledger Technologies (DLTs), when managed by a few trusted
validators, require most but not all of the machinery available in public DLTs.
In this work, we explore one possible way to profit from this state of affairs.
We devise a combination of a modified Practical Byzantine Fault Tolerant (PBFT)
protocol and a revised Flexible Round-Optimized Schnorr Threshold Signatures
(FROST) scheme, and then we inject the resulting proof-of-authority consensus
algorithm into Bitcoin (chosen for the reliability, openness, and liveliness it
brings in), replacing its PoW machinery. The combined protocol may operate as a
modern, safe foundation for digital payment systems and Central Bank Digital
Currencies (CBDC)
Blockchain-based Security Framework for Critical Industry 4.0 Cyber-physical System
There has been an intense concern for security alternatives because of the
recent rise of cyber attacks, mainly targeting critical systems such as
industry, medical, or energy ecosystem. Though the latest industry
infrastructures largely depend on AI-driven maintenance, the prediction based
on corrupted data undoubtedly results in loss of life and capital. Admittedly,
an inadequate data-protection mechanism can readily challenge the security and
reliability of the network. The shortcomings of the conventional cloud or
trusted certificate-driven techniques have motivated us to exhibit a unique
Blockchain-based framework for a secure and efficient industry 4.0 system. The
demonstrated framework obviates the long-established certificate authority
after enhancing the consortium Blockchain that reduces the data processing
delay, and increases cost-effective throughput. Nonetheless, the distributed
industry 4.0 security model entails cooperative trust than depending on a
single party, which in essence indulges the costs and threat of the single
point of failure. Therefore, multi-signature technique of the proposed
framework accomplishes the multi-party authentication, which confirms its
applicability for the real-time and collaborative cyber-physical system.Comment: 07 Pages, 4 Figures, IEEE Communication Magazin
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing
Federated Learning (FL) has gained widespread popularity in recent years due
to the fast booming of advanced machine learning and artificial intelligence
along with emerging security and privacy threats. FL enables efficient model
generation from local data storage of the edge devices without revealing the
sensitive data to any entities. While this paradigm partly mitigates the
privacy issues of users' sensitive data, the performance of the FL process can
be threatened and reached a bottleneck due to the growing cyber threats and
privacy violation techniques. To expedite the proliferation of FL process, the
integration of blockchain for FL environments has drawn prolific attention from
the people of academia and industry. Blockchain has the potential to prevent
security and privacy threats with its decentralization, immutability,
consensus, and transparency characteristic. However, if the blockchain
mechanism requires costly computational resources, then the
resource-constrained FL clients cannot be involved in the training. Considering
that, this survey focuses on reviewing the challenges, solutions, and future
directions for the successful deployment of blockchain in resource-constrained
FL environments. We comprehensively review variant blockchain mechanisms that
are suitable for FL process and discuss their trade-offs for a limited resource
budget. Further, we extensively analyze the cyber threats that could be
observed in a resource-constrained FL environment, and how blockchain can play
a key role to block those cyber attacks. To this end, we highlight some
potential solutions towards the coupling of blockchain and federated learning
that can offer high levels of reliability, data privacy, and distributed
computing performance
Electricity and Blockchain: How Advanced Technologies are Activating Peer-to-Peer Energy Markets
66 pagesBlockchain technology has been at the forefront of technological innovation for several years and presents a fascinating emerging technology to assess. Due to its cost savings, security, and reduced transaction times the technology has the potential for substantial disruption. The energy industry happens to be ripe for disruption, as an industry that has undergone a distinct lack of innovation over the last several decades. The goal of this research piece is to produce a technical snapshot of how blockchain is enabling innovation in the energy sector. In particular, I assess the use of blockchain in activating novel peer-to-peer energy markets. As primary sources, I assess three business whitepapers of early moving startups in this space. Rather than performing a wide analysis, I focus on creating a granular technical review and assessment of these three case studies. Following a careful evaluation, I triangulate the results and detail the benefit of dual-layered blockchain platforms, which consensus protocols appear to be leading, and how early movers are developing competitive advantages. I also expand my findings to recommendations for new entrants in this space. I recommend utilizing the tokenization of energy, using AI and machine learning to remove human agency, and employing centralized actors as network decision makers
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