SoK: The Ghost Trilemma

• URL: http://arxiv.org/abs/2308.02202v3
• Date: Fri, 19 Jan 2024 10:12:11 GMT
• Title: SoK: The Ghost Trilemma
• Authors: Sulagna Mukherjee, Srivatsan Ravi, Paul Schmitt, Barath Raghavan
• Abstract summary: Trolls, bots, and sybils distort online discourse and compromise the security of networked platforms. We posit the Ghost Trilemma, that there are three key properties of identity that cannot be simultaneously verified in a fully-decentralized setting.
• Score: 4.751886527142779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trolls, bots, and sybils distort online discourse and compromise the security of networked platforms. User identity is central to the vectors of attack and manipulation employed in these contexts. However it has long seemed that, try as it might, the security community has been unable to stem the rising tide of such problems. We posit the Ghost Trilemma, that there are three key properties of identity -- sentience, location, and uniqueness -- that cannot be simultaneously verified in a fully-decentralized setting. Many fully-decentralized systems -- whether for communication or social coordination -- grapple with this trilemma in some way, perhaps unknowingly. In this Systematization of Knowledge (SoK) paper, we examine the design space, use cases, problems with prior approaches, and possible paths forward. We sketch a proof of this trilemma and outline options for practical, incrementally deployable schemes to achieve an acceptable tradeoff of trust in centralized trust anchors, decentralized operation, and an ability to withstand a range of attacks, while protecting user privacy.

Related papers

• Blockchain Takeovers in Web 3.0: An Empirical Study on the TRON-Steem Incident [11.681753873893173]
  We present a thorough empirical analysis of the Tron-Steem takeover incident. We quantify the marked shifts in decentralization pre and post the takeover incident.
  arXiv  Detail & Related papers  (2024-07-25T07:31:15Z)
• A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain [29.968086297894626]
  We propose a framework named DFLoc to achieve precise 3D localization tasks. Specifically, we address the issue of single-point failure for a reliable and accurate indoor localization system. We introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks.
  arXiv  Detail & Related papers  (2024-07-08T04:14:19Z)
• Decentralized Credential Status Management: A Paradigm Shift in Digital Trust [0.0]
  Public key infrastructures are essential for Internet security, ensuring robust certificate management and revocation mechanisms. The transition from centralized to decentralized systems presents challenges such as trust distribution and privacy-preserving credential management. This paper explores the evolution of certificate status management from centralized to decentralized frameworks, focusing on blockchain technology and advanced cryptography.
  arXiv  Detail & Related papers  (2024-06-17T13:17:56Z)
• AI and Democracy's Digital Identity Crisis [0.0]
  Privacy-preserving identity attestations can drastically reduce instances of impersonation and make disinformation easy to identify and potentially hinder. In this paper, we discuss attestation types, including governmental, biometric, federated, and web of trust-based. We believe these systems could be the best approach to authenticating identity and protecting against some of the threats to democracy that AI can pose in the hands of malicious actors.
  arXiv  Detail & Related papers  (2023-09-25T14:15:18Z)
• A Note on the Blockchain Trilemma for Decentralized Identity: Learning from Experiments with Hyperledger Indy [0.38073142980732994]
  We explore the blockchain trilemma of scalability, security, and decentralization in the context of the Trust Registry. We make a case study of Hyperledger Indy, an open-source technology bespoke for decentralized identity. While Hyperledger Indy captures data useful to underpin a decentralized identity scheme, the knock-on effect of its scalability limitations may indeed place constraints on properties of security and decentralization.
  arXiv  Detail & Related papers  (2022-04-12T13:12:49Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• Secure Distributed Training at Scale [65.7538150168154]
  Training in presence of peers requires specialized distributed training algorithms with Byzantine tolerance. We propose a novel protocol for secure (Byzantine-tolerant) decentralized training that emphasizes communication efficiency.
  arXiv  Detail & Related papers  (2021-06-21T17:00:42Z)
• Perceiving Humans: from Monocular 3D Localization to Social Distancing [93.03056743850141]
  We present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule.
  arXiv  Detail & Related papers  (2020-09-01T10:12:30Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)
• Byzantine-resilient Decentralized Stochastic Gradient Descent [85.15773446094576]
  We present an in-depth study towards the Byzantine resilience of decentralized learning systems. We propose UBAR, a novel algorithm to enhance decentralized learning with Byzantine Fault Tolerance.
  arXiv  Detail & Related papers  (2020-02-20T05:11:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.