3 Search Results for "Knight, Philip A."

Document
DOI: 10.4230/LIPIcs.AFT.2024.28
SoK: Attacks on DAOs

Authors: Rainer Feichtinger, Robin Fritsch, Lioba Heimbach, Yann Vonlanthen, and Roger Wattenhofer

Published in: LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024)

Abstract
Decentralized Autonomous Organizations (DAOs) are blockchain-based organizations that facilitate decentralized governance. Today, DAOs not only hold billions of dollars in their treasury but also govern many of the most popular Decentralized Finance (DeFi) protocols. This paper systematically analyses security threats to DAOs, focusing on the types of attacks they face. We study attacks on DAOs that took place in the past, attacks that have been theorized to be possible, and potential attacks that were uncovered and prevented in audits. For each of these (potential) attacks, we describe and categorize the attack vectors utilized into four categories. This reveals that while many attacks on DAOs take advantage of the less tangible and more complex human nature involved in governance, audits tend to focus on code and protocol vulnerabilities. Thus, additionally, the paper examines empirical data on DAO vulnerabilities, outlines risk factors contributing to these attacks, and suggests mitigation strategies to safeguard against such vulnerabilities.
Cite as

Rainer Feichtinger, Robin Fritsch, Lioba Heimbach, Yann Vonlanthen, and Roger Wattenhofer. SoK: Attacks on DAOs. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 28:1-28:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{feichtinger_et_al:LIPIcs.AFT.2024.28,
  author =	{Feichtinger, Rainer and Fritsch, Robin and Heimbach, Lioba and Vonlanthen, Yann and Wattenhofer, Roger},
  title =	{{SoK: Attacks on DAOs}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{28:1--28:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-345-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{316},
  editor =	{B\"{o}hme, Rainer and Kiffer, Lucianna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024.28},
  URN =		{urn:nbn:de:0030-drops-209640},
  doi =		{10.4230/LIPIcs.AFT.2024.28},
  annote =	{Keywords: blockchain, DAO, governance, security, measurements, voting systems}
}
Document
DOI: 10.4230/DagSemProc.07071.4
A Fast Algorithm for Matrix Balancing

Authors: Philip A. Knight and Daniel Ruiz

Published in: Dagstuhl Seminar Proceedings, Volume 7071, Web Information Retrieval and Linear Algebra Algorithms (2007)

Abstract
As long as a square nonnegative matrix A contains sufficient nonzero elements, then the matrix can be balanced, that is we can find a diagonal scaling of A that is doubly stochastic. A number of algorithms have been proposed to achieve the balancing, the most well known of these being the Sinkhorn-Knopp algorithm. In this paper we derive new algorithms based on inner-outer iteration schemes. We show that the Sinkhorn-Knopp algorithm belongs to this family, but other members can converge much more quickly. In particular, we show that while stationary iterative methods offer little or no improvement in many cases, a scheme using a preconditioned conjugate gradient method as the inner iteration can give quadratic convergence at low cost.
Cite as

Philip A. Knight and Daniel Ruiz. A Fast Algorithm for Matrix Balancing. In Web Information Retrieval and Linear Algebra Algorithms. Dagstuhl Seminar Proceedings, Volume 7071, pp. 1-18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{knight_et_al:DagSemProc.07071.4,
  author =	{Knight, Philip A. and Ruiz, Daniel},
  title =	{{A Fast Algorithm for Matrix Balancing}},
  booktitle =	{Web Information Retrieval and Linear Algebra Algorithms},
  pages =	{1--18},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7071},
  editor =	{Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.4},
  URN =		{urn:nbn:de:0030-drops-10735},
  doi =		{10.4230/DagSemProc.07071.4},
  annote =	{Keywords: Matrix balancing, Sinkhorn-Knopp algorithm, doubly stochastic matrix, conjugate gradient iteration}
}
Document
DOI: 10.4230/DagSemProc.07071.16
The Sinkhorn-Knopp Algorithm:Convergence and Applications

Authors: Philip A. Knight

Published in: Dagstuhl Seminar Proceedings, Volume 7071, Web Information Retrieval and Linear Algebra Algorithms (2007)

Abstract
As long as a square nonnegative matrix $A$ contains sufficient nonzero elements, the Sinkhorn-Knopp algorithm can be used to balance the matrix, that is, to find a diagonal scaling of $A$ that is doubly stochastic. We relate balancing to problems in traffic flow and describe how balancing algorithms can be used to give a two sided measure of nodes in a graph. We show that with an appropriate modification, the Sinkhorn-Knopp algorithm is a natural candidate for computing the measure on enormous data sets.
Cite as

Philip A. Knight. The Sinkhorn-Knopp Algorithm:Convergence and Applications. In Web Information Retrieval and Linear Algebra Algorithms. Dagstuhl Seminar Proceedings, Volume 7071, pp. 1-18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{knight:DagSemProc.07071.16,
  author =	{Knight, Philip A.},
  title =	{{The Sinkhorn-Knopp Algorithm:Convergence and Applications}},
  booktitle =	{Web Information Retrieval and Linear Algebra Algorithms},
  pages =	{1--18},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7071},
  editor =	{Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.16},
  URN =		{urn:nbn:de:0030-drops-10644},
  doi =		{10.4230/DagSemProc.07071.16},
  annote =	{Keywords: Matrix balancing, Sinkhorn-Knopp algorithm, PageRank, doubly stochastic matrix}
}
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