AI Overseer Trust Scoring for Peer Network Access
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Solution Overview
Problem
Existing peer networks lack the ability to verify trust on a user-to-user basis and fail to monitor the trustworthiness of members after admission, making them vulnerable to malicious actors.
Innovation Solution
Implement an overseer using artificial intelligence to calculate a trustworthiness score for untrusted nodes based on their activity within the network, restrict their participation, and designate them as trusted when they adhere to network goals, while using bot nodes to counter untrusted voting activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If new nodes are added to the peer network without verification, then the network grows and becomes more active, but the network becomes vulnerable to malicious actors and trust issues
Solution Approach 1:
The system performs preliminary trust verification and node vetting before allowing full network participation. New nodes undergo a verification process that includes background checks, identity validation, and trust scoring before being granted full access to network resources and voting rights, preventing malicious actors from immediately compromising the network
Solution Approach 2:
The patent introduces intermediary verification mechanisms including trusted introducers, vote counters, and oversight committees that mediate between new nodes and the existing network. These intermediaries validate node identities, monitor initial activities, and gradually integrate verified nodes into the network, creating a buffer that protects the network from unverified malicious actors
2Reliability
If an overseer system is implemented to monitor and calculate trustworthiness scores, then network security and trust are improved, but system complexity increases
Solution Approach 1:
The overseer system automatically performs trust scoring, node verification, and threat detection without requiring manual intervention. The system self-manages the complexity of monitoring network activities, calculating trustworthiness scores based on observed behaviors, and dynamically adjusting node permissions, thereby containing complexity within the automated system while maintaining network security
Solution Approach 2:
The patent implements continuous feedback loops where the overseer system monitors node activities, updates trust scores in real-time, and adjusts node permissions based on observed behavior. This automated feedback mechanism handles the complexity of trust management dynamically, allowing the system to adapt to new threats and verify nodes without proportional increases in operational complexity
3Reliability
If untrusted nodes are restricted from full participation, then network integrity is protected, but network activity and contribution from new nodes are reduced
Solution Approach 1:
The system dynamically adjusts node permissions and participation levels based on verified trust scores rather than applying static restrictions. Nodes progress through graduated access levels where verified nodes gain increased privileges, allowing them to contribute more significantly to network activities as their trustworthiness is confirmed, thereby maintaining integrity while enabling productive participation
4Reliability
If bot nodes are deployed to counter untrusted voting activity, then voting integrity is maintained, but system complexity and resource consumption increase
Solution Approach 1:
The patent deploys bot nodes as preliminary countermeasures against potential voting manipulation by untrusted nodes. These bots are positioned in advance to detect and neutralize coordinated voting attacks, manipulating behavior patterns, thereby protecting voting integrity while consuming computational resources proactively rather than reactively
Data Source
AI summary
Legitimizing trust in peer networks using artificial intelligence, including: calculating, by an overseer, a trustworthiness score of an untrusted node of a peer network comprising a plurality of nodes, wherein the trustworthiness score is based on activity of the untrusted node in the peer network; and restricting participation in the peer network by the untrusted node based on the untrusted node being flagged as untrusted.