Do you need a blockchain in construction? Use case categories and decision framework for DLT design options
March 31, 2020 Β· Declared Dead Β· π Advanced Engineering Informatics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jens J. Hunhevicz, Daniel M. Hall
arXiv ID
2004.04626
Category
cs.CR: Cryptography & Security
Citations
193
Venue
Advanced Engineering Informatics
Last Checked
4 months ago
Abstract
Blockchain and other forms of distributed ledger technology (DLT) provide an opportunity to integrate digital information, management, and contracts to increase trust and collaboration within the construction industry. DLT enables direct peer-to-peer transactions of value across a distributed network by providing an immutable and transparent record of these transactions. Furthermore, there is potential for business process optimization and automation on the transaction level, through the use of smart contracts, which are code protocols deployed on supported DLT systems. However, DLT research in the construction industry remains at a theoretical level; there have been few implementation case studies to date. One potential reason for this is a knowledge gap between use-case ideas and the DLT technical system implementation. This paper aims to reduce this gap by 1) reviewing and categorizing proposed DLT use cases in construction literature, 2) providing an overview of DLT and its design options, 3) proposing an integrated framework to match DLT design options with desired characteristics of a use case, and 4) analysing the use cases using the new framework. Together, the use case categories and proposed decision framework can guide future implementers toward more connected and structured thinking between the technological properties of DLT and use cases in construction.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted