Using Trust in Distributed Consensus with Adversaries in Sensor and Other Networks Xiangyang Liu,...
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Transcript of Using Trust in Distributed Consensus with Adversaries in Sensor and Other Networks Xiangyang Liu,...
![Page 1: Using Trust in Distributed Consensus with Adversaries in Sensor and Other Networks Xiangyang Liu, and John S. Baras Institute for Systems Research and.](https://reader036.fdocuments.net/reader036/viewer/2022062517/56649e745503460f94b756dd/html5/thumbnails/1.jpg)
Using Trust in Distributed Consensus with Adversaries in Sensor and Other Networks
Xiangyang Liu, and John S. Baras
Institute for Systems Research and
Department of Electrical and Computer Engineering
University of Maryland, College Park, MD
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Outline
Introduction Motivation Trust-Aware Consensus Simulations Conclusion
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Introduction
CooperationCooperation
Cooperation
Agent
Agent Agent
Agent
Cooperation
Co
op
era
tion
• Distributed sensor fusion. Goal: all agents reach consensus on ML estimate.
[1] Xiao, Lin, Stephen Boyd, and Sanjay Lall. "A scheme for robust distributed sensor fusion based on average consensus." Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Symposium on. IEEE, 2005.
• Distributed Coordination. Goal: all agents reach decision on same direction (location)
[2] Jadbabaie, Ali, Jie Lin, and A. Stephen Morse. "Coordination of groups of mobile autonomous agents using nearest neighbor rules." Automatic Control, IEEE Transactions on 48.6 (2003): 988-1001.
Agent
Without supervisor
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Introduction
CooperationCooperation
Cooperation
Agent
Agent
Agent
Cooperation
Co
op
era
tion
Agent
Link Jam & Noise Injection:
[3]Khanafer, Ali, Behrouz Touri, and Tamer Basar. "Consensus in the presence of an adversary." 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys). 2012.
Malicious Agent
Malicious agent:• Multiparty secure computation[4] Garay, Juan A., and Rafail Ostrovsky. "Almost-everywhere secure computation." Advances in Cryptology–EUROCRYPT 2008. Springer Berlin Heidelberg, 2008. 307-323.• Consensus with Byzantine adversaries (System theory)[5] Pasqualetti, Fabio, Antonio Bicchi, and Francesco Bullo. "Consensus computation in unreliable networks: A system theoretic approach." Automatic Control, IEEE Transactions on 57.1 (2012): 90-104.
trust
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Motivation
Good Node Malicious Node
Goal: Detect malicious nodes and isolate them from consensus algorithm.
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Trust-Aware Consensus
Good Node
Malicious Node
Trust Evidence
Local Trust
Decision rules
Global Trust
Trust Propagation
Trust-Aware Consensus
Embed trust into consensus
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Trust-Aware Consensus
Trust Evidence
Local Trust
Node i’s trust evidence:
Clustering-Based
Distance-Based
Consistency-BasedDecision rules:
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Trust-Aware Consensus
Clustering-Based
Distance-Based
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Trust-Aware Consensus
Consistency-Based
message broadcast by node l and heard by node i
message broadcast by node j about what it hears from node l
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Trust-Aware Consensus
Local Trust
Global Trust
Trust Propagation
3
4
5
MaliciousNormal Header
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Trust-Aware Consensus
Trust Evidence
Local Trust
Decision rules
Global Trust
Trust Propagation
Trust-Aware Consensus
Embed trust into consensus
![Page 12: Using Trust in Distributed Consensus with Adversaries in Sensor and Other Networks Xiangyang Liu, and John S. Baras Institute for Systems Research and.](https://reader036.fdocuments.net/reader036/viewer/2022062517/56649e745503460f94b756dd/html5/thumbnails/12.jpg)
Simulations
Adversary outputs constant message. Figure on the left has no trust propagation. Figure on the right has trust propagation.
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Simulations
Adversary switches randomly between several messages. Figure on the left has no trust propagation. Figure on the right has trust propagation.
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Simulations
Adversary takes random noise strategy. Figure on the left has no trust propagation. Figure on the right has trust propagation.
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Simulations
Adversary takes fixed noise strategy. Figure on the left has no trust propagation. Figure on the right has trust propagation.
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Simulations
3
4
5
Left: adversary takes constant strategy. Right: adversary takes random noise strategy.
The communication graph has connectivity 2.
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Conclusion
• Developed trust model with various decision rules based
on local evidence in the setting of Byzantine adversaries.
• Trust-Aware consensus algorithm proposed is flexible
and can be extended to incorporate more complicated
trust models and decision rules.
• Simulations show our algorithm can effectively detect
malicious strategies even in sparse networks of
connectivity , where is the number of adversaries.
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Thank you!
{xyliu, baras}@umd.edu
http://www.isr.umd.edu/~baras
Questions?