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Joshua Holmes

dblp:260/5395 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
0009-0007-9223-6385ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › consensus
blockchain consensus
0.612022
ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022
Distributed systems
consensus
0.612022
ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022
Distributed systems › consensus › blockchain consensus
permissioned consensus
0.612022
ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022
Distributed systems › replication › replica control
quorum consensus
0.612022
ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022
Medical and health informatics
electronic health records
0.212022
ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022

Methods — techniques the papers use, named apart from their topics

voting-based arbitration · 1.1quorum selection · 1.1
YearPublicationVenuePosition
2022 ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain
abstract
Blockchain is an emerging distributed and decentralized technology that promises to revolutionize the healthcare sector by securely storing and maintaining incorruptible electronic health record data. Consensus protocols are at the core of blockchain technology. They establish security and integrity in the system by ensuring that the majority of miners are in agreement on all transactions and blocks added to the distributed ledger. While many consensus protocols have been proposed, most of them require heavy computation and are not scalable. In this work, we propose a novel permissioned consensus protocol, named ACCORD, a multi-leader (quorum-based) protocol that achieves fork-resistance, robustness, and scalability. To achieve this, ACCORD consists of three distinct components: (1) an asynchronous quorum selection procedure to designate the creators of future blocks, (2) a block creation protocol run by the quorum to prevent omissions in the presence of honest quorum members, and (3) a decentralized arbitration protocol to ensure consensus by voting. Additionally, we implemented the protocol and conducted experiments to demonstrate scalability, robustness, and fairness.
Golam Dastoger Bashar, Joshua Holmes, Gaby G. Dagher
IEEE Trans. Inf. Forensics Secur.2
2020 ZeroLender: Trustless Peer-to-Peer Bitcoin Lending Platform
abstract
Since its inception a decade ago, Bitcoin and its underlying blockchain technology have been garnering interest from a large spectrum of financial institutions. Although it encompasses a currency, a payment method, and a ledger, Bitcoin as it currently stands does not support bitcoins lending. In this paper, we present a platform called ZeroLender for peer-to-peer lending in Bitcoin. Our protocol utilizes zero-knowledge proofs to achieve unlinkability between lenders and borrowers while securing payments in both directions against potential malicious behaviour of the ZeroLender as well as the lenders, and prove by simulation that our protocol is privacy-preserving. Based on our experiments, we show that the runtime and transcript size of our protocol scale linearly with respect to the number of lenders and repayments.
Yi Xie 0008, Joshua Holmes, Gaby G. Dagher
CODASPY2