Wearable Transaction Encryption Using Neural Sequencing and Blockchain
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Solution Overview
Problem
Existing technologies face challenges in securely encrypting user device resource transactions, particularly with wearable devices, due to vulnerabilities such as PIN sequence inference algorithms and sensor data misappropriation through malware or eavesdropping, which compromise user data privacy.
Innovation Solution
The system employs recurrent neural networks to sequence user data, encrypts it using lattice-based multivariate polynomial encryption, and stores it in a blockchain validated by a Proof-of-Authority consensus algorithm, ensuring secure data storage and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption methods are used for user device resource transactions, then implementation is simpler, but security is compromised due to vulnerabilities such as PIN sequence inference algorithms and sensor data misappropriation
Solution Approach 1:
The encryption system is segmented into multiple independent components: a neural network for sequencing user data, a lattice-based multivariate polynomial encryption module, and a blockchain storage system. Each component performs a specific function, and together they form a comprehensive security solution that addresses the vulnerabilities of traditional single-method encryption approaches.
2Reliability
If advanced encryption methods like lattice-based multivariate polynomial encryption are implemented, then security is improved, but computing resources are consumed
Solution Approach 1:
The neural network sequences user data before encryption, organizing the data in advance in a unique sequence. This preliminary organization optimizes the subsequent encryption process, allowing the lattice-based multivariate polynomial encryption to operate more efficiently on pre-structured data, thereby reducing overall computing resource consumption.
3Productivity
If manual input processes are used in the encryption system, then implementation is easier, but accuracy and efficiency are reduced
Solution Approach 1:
The system automatically sequences user data through the neural network and performs encryption without requiring manual intervention. The process is self-executing, with the system managing data transformation and encryption autonomously, which eliminates manual input errors and significantly improves encryption efficiency while maintaining operational simplicity through automated workflows.
Data Source
AI summary
Systems, computer program products, and methods are described herein for encrypting user device resource transactions. The present disclosure is configured to transmit user data to a neural network, wherein the user data includes gesture data, and wherein the neural network sequences the user data in a unique sequence. Further, the present disclosure is configured to encrypt the user data to create encrypted user data, wherein the encrypted user data includes encrypted gesture data, and wherein encrypting the user data comprises a lattice-based multivariate polynomial encryption. Further, the present disclosure is configured to store the encrypted user data in a blockchain. Further, the present disclosure is configured to validate the blockchain with a consensus algorithm.