Who Watches the Watchmen? A Review of Subjective Approaches for Sybil-resistance in Proof of Personhood Protocols
July 26, 2020 Β· Declared Dead Β· π Frontiers in Blockchain
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Divya Siddarth, Sergey Ivliev, Santiago Siri, Paula Berman
arXiv ID
2008.05300
Category
cs.CR: Cryptography & Security
Citations
39
Venue
Frontiers in Blockchain
Last Checked
6 months ago
Abstract
Most current self-sovereign identity systems may be categorized as strictly objective, consisting of cryptographically signed statements issued by trusted third party attestors. This failure to provide an input for subjectivity accounts for a central challenge: the inability to address the question of "Who verifies the verifier?". Instead, these protocols outsource their legitimacy to mechanisms beyond their internal structure, relying on traditional centralized institutions such as national ID issuers and KYC providers to verify the claims they hold. This reliance has been employed to safeguard applications from a vulnerability previously thought to be impossible to address in distributed systems: the Sybil attack problem, which describes the abuse of an online system by creating many illegitimate virtual personas. Inspired by the progress in cryptocurrencies and blockchain technology, there has recently been a surge in networked protocols that make use of subjective inputs such as voting, vouching, and interpreting, to arrive at a decentralized and sybil-resistant consensus for identity. In this article, we will outline the approaches of these new and natively digital sources of authentication -- their attributes, methodologies strengths, and weaknesses -- and sketch out possible directions for future developments.
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