Fail-safe Watchtowers and Short-lived Assertions for Payment Channels
March 13, 2020 Β· Declared Dead Β· π ACM Asia Conference on Computer and Communications Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bowen Liu, Pawel Szalachowski, Siwei Sun
arXiv ID
2003.06127
Category
cs.CR: Cryptography & Security
Citations
13
Venue
ACM Asia Conference on Computer and Communications Security
Last Checked
5 months ago
Abstract
The recent development of payment channels and their extensions (e.g., state channels) provides a promising scalability solution for blockchains which allows untrusting parties to transact off-chain and resolve potential disputes via on-chain smart contracts. To protect participants who have no constant access to the blockchain, a watching service named as watchtower is proposed -- a third-party entity obligated to monitor channel states (on behalf of the participants) and correct them on-chain if necessary. Unfortunately, currently proposed watchtower schemes suffer from multiple security and efficiency drawbacks. In this paper, we explore the design space behind watchtowers. We propose a novel watching service named as fail-safe watchtowers. In contrast to prior proposed watching services, our fail-safe watchtower does not watch on-chain smart contracts constantly. Instead, it only sends a single on-chain message periodically confirming or denying the final states of channels being closed. Our watchtowers can easily handle a large number of channels, are privacy-preserving, and fail-safe tolerating multiple attack vectors. Furthermore, we show that watchtowers (in general) may be an option economically unjustified for multiple payment scenarios and we introduce a simple, yet powerful concept of short-lived assertions which can mitigate misbehaving parties in these scenarios.
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