Blockchain Database Risk Score Module for Anomaly Detection
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Solution Overview
Problem
Centralized databases face issues such as single points of failure, dependency on network connectivity, limited data access, and difficulty in retrieving lost data due to lack of redundancy, leading to potential data loss and system interruptions.
Innovation Solution
Implementing a decentralized database system using a blockchain network with a risk score module to detect and flag anomalous transactions, ensuring data redundancy and security through a distributed ledger that maintains records across multiple nodes, allowing for consensus-based validation and endorsement of transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized database is used to store and maintain data at a single location, then data redundancy is minimized and management is simplified, but the system has a single point of failure and limited fault tolerance
Solution Approach 1:
The patent divides the centralized database into multiple distributed nodes across a network. Each node maintains a copy of the ledger, eliminating the single point of failure. The segmentation transforms one centralized storage location into many distributed storage locations, improving reliability while maintaining manageable complexity through automated consensus protocols.
Solution Approach 2:
The patent creates multiple copies of the data ledger across different nodes in the network. Instead of storing data at a single location, each node maintains an identical copy through the consensus mechanism. This copying approach ensures that if one node fails, the data remains accessible from other nodes, thereby improving fault tolerance.
2Ease of operation
If a centralized database maintains a single copy of data, then access control is simplified, but multiple devices cannot access the same data simultaneously without creating conflicts or overwriting risks
Solution Approach 1:
The patent segments the data access function across multiple independent nodes. Each node can independently validate and process transactions simultaneously through the consensus mechanism. This segmentation allows multiple devices to access and write data at the same time without conflicts, as each node handles access requests independently and reaches consensus on the state.
Solution Approach 2:
The patent implements a feedback mechanism where nodes communicate transaction validity to the network. When a node receives a data access request, it validates the transaction and provides feedback to other nodes through the consensus protocol. This feedback loop ensures simultaneous access is coordinated without conflicts, maintaining data integrity while enabling parallel access operations.
3Device complexity
If a centralized database is used, then network connectivity dependency increases access time, but the system structure remains simple
Solution Approach 1:
The patent segments the database system into multiple geographically distributed nodes. Users can access data from the nearest node rather than a single centralized location, reducing network latency. The segmentation allows parallel data retrieval from multiple nodes simultaneously, decreasing overall access time while maintaining a relatively simple system structure through standardized node interfaces.
4Ease of operation
If a centralized database experiences high traffic, then bottlenecks occur at the single location, but the system is easier to manage
Solution Approach 1:
The patent segments the transaction processing load across multiple nodes in the network. Instead of all transactions passing through a single centralized database, each node independently validates and processes transactions in parallel. This segmentation eliminates bottlenecks by distributing the processing throughput across the network while maintaining manageable complexity through automated consensus protocols that require minimal manual intervention.
Solution Approach 2:
The patent implements self-service mechanisms where nodes automatically validate transactions and reach consensus without centralized coordination. Each node independently processes incoming transactions, validates them against the ledger state, and communicates results to other nodes. This self-service approach increases transaction processing throughput by eliminating single-point bottlenecks while keeping system management simple through automated conflict resolution.
Data Source
AI summary
An example operation may include one or more of receiving, by a risk score module, a blockchain transaction proposal, obtaining transaction proposal data, obtaining external data, computing a risk score from the transaction proposal data and the external data, comparing the risk score to a risk score threshold, providing an endorsement decision, based on the comparison, and one of endorsing or rejecting the transaction proposal.