Quantum Feature Encoding for Fraudulent Authentication Detection
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Solution Overview
Problem
Existing identity proofing and fraud detection techniques fail to accurately detect subtle generative adversarial network-based anomalies and nuanced patterns in high-dimensional biometric data, are vulnerable to multi-modal fraudulent data, and are affected by environmental noise and constrained feature sets, leading to inaccurate results and loss of efficacy.
Innovation Solution
A method involving an electronic device that computes a feature vector from received data, normalizes it, encodes it into qubits, and expands it using quantum algorithms into a high-dimensional space to detect anomalies, with error mitigation techniques and post-quantum cryptography for enhanced fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based engines and neural networks are used for fraud detection, then the system can process authentication data, but it fails to detect subtle generative adversarial network-based anomalies and nuanced patterns in high-dimensional biometric data
Solution Approach 1:
The patent replaces classical rule-based engines and neural networks with a quantum computing system that uses qubits and quantum algorithms to process authentication data. This substitution enables the system to detect subtle GAN-based anomalies and nuanced patterns in high-dimensional biometric data through quantum mechanical processes, achieving superior detection accuracy without proportionally increasing system complexity
Solution Approach 2:
The patent encodes authentication data into quantum states in a high-dimensional Hilbert space, allowing the quantum system to capture and analyze nuanced patterns that are invisible to classical two-dimensional data representations. This dimensional transformation enables detection of subtle anomalies while maintaining computational efficiency
2Measurement precision
If quantum algorithms are used to expand feature vectors into high-dimensional space, then subtle anomalies can be detected, but noise-induced inaccuracies are introduced by Noisy Intermediate-Scale Quantum hardware
Solution Approach 1:
The patent converts the harmful noise from NISQ hardware into a beneficial signal by training a classical neural network to recognize noise-induced patterns as indicators of GAN-generated data. The system learns that specific noise patterns correlate with fraudulent authentication data, transforming hardware limitations into a detection advantage
Solution Approach 2:
The patent introduces a classical neural network as an intermediary between the quantum hardware and the final detection output. This intermediary layer processes and interprets the quantum measurements, filtering out noise-induced inaccuracies while preserving genuine anomaly signals, thereby reconciling quantum capabilities with reliable results
3Measurement precision
If quantum computing is used for fraud detection, then detection accuracy improves, but the system requires error mitigation techniques and post-quantum cryptography
Solution Approach 1:
The patent merges quantum computing capabilities with classical machine learning techniques into a hybrid system. The quantum processor handles high-dimensional feature space exploration and anomaly detection, while classical neural networks manage noise mitigation and pattern recognition. This combination achieves superior fraud detection accuracy while distributing complexity across different computational paradigms
Data Source
AI summary
A method for enhancing detection of fraudulent authentication data includes receiving, by an electronic device, data during an authentication transaction, computing a feature vector from the received data, and normalizing the feature vector. The method also includes encoding the normalized feature vector into qubits, expanding, using at least one quantum algorithm, the normalized feature vector into a high-dimensional space, and detecting in the high-dimensional space anomalies indicative of fraud based on the qubits. Furthermore, the method includes calculating, based on the detected anomalies, a confidence score reflecting a likelihood that the received data is genuine and comparing the confidence score against a threshold value. In response to determining the confidence score fails to satisfy the threshold value, the method determines that the received data requires secondary authentication.