Demo Data from VLDB 2026 / reviewers in the wild / expert
Joshua Holmes
dblp:260/5395
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › consensus
blockchain consensus |
0.6 | 1 | 2022 | ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022 |
Distributed systems
consensus |
0.6 | 1 | 2022 | ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022 |
Distributed systems › consensus › blockchain consensus
permissioned consensus |
0.6 | 1 | 2022 | ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022 |
Distributed systems › replication › replica control
quorum consensus |
0.6 | 1 | 2022 | ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain · IEEE Trans. Inf. Forensics Secur. 2022 |
Medical and health informatics
electronic health records |
0.2 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | ACCORD: A Scalable Multileader Consensus Protocol for Healthcare BlockchainabstractBlockchain 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 PlatformabstractSince 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 |
CODASPY | 2 |