Daml: A Smart Contract Language for Securely Automating Real-World Multi-Party Business Workflows
March 07, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Alexander Bernauer, Sofia Faro, RΓ©my HΓ€mmerle, Martin Huschenbett, Moritz Kiefer, Andreas Lochbihler, Jussi MΓ€ki, Francesco Mazzoli, Simon Meier, Neil Mitchell, Ratko G. Veprek
arXiv ID
2303.03749
Category
cs.PL: Programming Languages
Citations
4
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Distributed ledger technologies, also known as blockchains for enterprises, promise to significantly reduce the high cost of automating multi-party business workflows. We argue that a programming language for writing such on-ledger logic should satisfy three desiderata: (1) Provide concepts to capture the legal rules that govern real-world business workflows. (2) Include simple means for specifying policies for access and authorization. (3) Support the composition of simple workflows into complex ones, even when the simple workflows have already been deployed. We present the open-source smart contract language Daml based on Haskell with strict evaluation. Daml achieves these desiderata by offering novel primitives for representing, accessing, and modifying data on the ledger, which are mimicking the primitives of today's legal systems. Robust access and authorization policies are specified as part of these primitives, and Daml's built-in authorization rules enable delegation, which is key for workflow composability. These properties make Daml well-suited for orchestrating business workflows across multiple, otherwise heterogeneous parties. Daml contracts run (1) on centralized ledgers backed by a database, (2) on distributed deployments with Byzantine fault tolerant consensus, and (3) on top of conventional blockchains, as a second layer via an atomic commit protocol.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
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