Resource-Aware Session Types for Digital Contracts
February 16, 2019 ยท Declared Dead ยท ๐ IEEE Computer Security Foundations Symposium
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ankush Das, Stephanie Balzer, Jan Hoffmann, Frank Pfenning, Ishani Santurkar
arXiv ID
1902.06056
Category
cs.PL: Programming Languages
Citations
60
Venue
IEEE Computer Security Foundations Symposium
Last Checked
2 months ago
Abstract
Programming digital contracts comes with unique challenges, which include (i) expressing and enforcing protocols of interaction, (ii) controlling resource usage, and (iii) preventing the duplication or deletion of a contract's assets. This article presents the design and type-theoretic foundation of Nomos, a programming language for digital contracts that addresses these challenges. To express and enforce protocols, Nomos is based on shared binary session types. To control resource usage, Nomos employs automatic amortized resource analysis. To prevent the duplication or deletion of assets, Nomos uses a linear type system. A monad integrates the effectful session-typed language with a general-purpose functional language. Nomos' prototype implementation features linear-time type checking and efficient type reconstruction that includes automatic inference of resource bounds via off-the-shelf linear optimization. The effectiveness of the language is evaluated with case studies about implementing common smart contracts such as auctions, elections, and currencies. Nomos is completely formalized, including the type system, a cost semantics, and a transactional semantics to instantiate Nomos contracts on a blockchain. The type soundness proof ensures that protocols are followed at run-time and that types establish sound upper bounds on the resource consumption, ruling out re-entrancy and out-of-gas vulnerabilities.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Programming Languages
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
๐ป
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
๐ป
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
๐ป
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
๐ป
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted