PREStO: A Systematic Framework for Blockchain Consensus Protocols
June 15, 2019 Β· Declared Dead Β· π IEEE transactions on engineering management
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Stefanos Leonardos, Daniel Reijsbergen, Georgios Piliouras
arXiv ID
1906.06540
Category
cs.CR: Cryptography & Security
Cross-listed
cs.GT
Citations
48
Venue
IEEE transactions on engineering management
Last Checked
6 months ago
Abstract
The rapid evolution of blockchain technology has brought together stakeholders from fundamentally different backgrounds. The result is a diverse ecosystem, as exemplified by the development of a wide range of different blockchain protocols. This raises questions for decision and policy makers: How do different protocols compare? What are their trade-offs? Existing efforts to survey the area reveal a fragmented terminology and the lack of a unified framework to reason about the properties of blockchain protocols. In this paper, we work towards bridging this gap. We present a five-dimensional design space with a modular structure in which protocols can be compared and understood. Based on these five axes -- Optimality, Stability, Efficiency, Robustness and Persistence -- we organize the properties of existing protocols in subcategories of increasing granularity. The result is a dynamic scheme -- termed the PREStO framework -- which aids the interaction between stakeholders of different backgrounds, including managers and investors, and which enables systematic reasoning about blockchain protocols. We illustrate its value by comparing existing protocols and identifying research challenges, hence making a first step towards understanding the blockchain ecosystem through a more comprehensive lens.
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