Federated Mining Multimodal Data Security Policies
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Solution Overview
Problem
Existing edge computing technologies face challenges in ensuring model robustness, data privacy, and security due to risks such as data theft, illegal access, and privacy disclosures, with existing security solutions like anonymous authentication and differential privacy still posing security threats.
Innovation Solution
A federated mining method for multimodal data based on multiple security policies, which includes a multiple authentication mechanism, a generalized multimodal data feature fusion model using a multi-head attention mechanism, and an adaptive perturbation mechanism based on cyclic correlation analysis and differential privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy protection method is used in edge computing, then privacy protection is improved, but computational burden increases and model optimization becomes local
Solution Approach 1:
The patent performs correlation analysis and identifies high-correlation parameter groups before the federated learning training process. By pre-processing and grouping parameters with high correlation coefficients, the system prepares the noise addition strategy in advance, avoiding complex real-time computations during training while maintaining privacy protection effectiveness.
Solution Approach 2:
The patent dynamically adjusts the noise scale parameter based on the correlation analysis results. Parameters with higher correlation coefficients receive smaller noise additions, while those with lower correlation receive larger noise additions. This adaptive parameter adjustment maintains privacy protection while reducing the overall computational burden compared to uniform noise addition.
2Reliability
If fusion encryption methods are used in edge computing, then data security is improved, but model computation efficiency decreases
Solution Approach 1:
The patent applies different noise addition strategies to different parameter groups based on their correlation characteristics. Instead of uniformly encrypting or protecting all parameters, the system identifies high-correlation groups and applies targeted perturbation only where necessary, leaving low-correlation parameters with minimal or no noise addition, thus improving training efficiency while maintaining security.
Solution Approach 2:
The patent adds noise selectively to parameter groups with lower correlation coefficients rather than adding noise to all parameters uniformly. This partial action approach focuses computational resources on protecting the most vulnerable parameters while skipping less sensitive ones, thereby improving model training efficiency without significantly compromising overall data security.
3Reliability
If access control and authentication are directly applied to edge computing, then security verification is improved, but computation and storage costs of edge nodes increase
Solution Approach 1:
The patent introduces a server-side correlation analysis mechanism that acts as an intermediary. The server performs the computationally intensive correlation analysis and determines the noise addition strategy, then sends instructions to edge nodes. This shifts the computational burden from resource-constrained edge nodes to the server, reducing edge node energy consumption while maintaining security verification.
Solution Approach 2:
The patent uses model parameters and their correlation coefficients as proxies for security verification instead of implementing complex authentication protocols at each edge node. By analyzing parameter correlations and applying perturbation based on these copies of the original data characteristics, the system verifies data quality and security without requiring heavy computational resources at edge nodes.
4Reliability
If existing security solutions like anonymous authentication are used, then privacy protection is improved, but vulnerability to privacy attacks increases
Solution Approach 1:
The patent combines multiple protection mechanisms: correlation analysis, selective noise addition based on correlation coefficients, and federated learning framework. This composite approach layers multiple defense strategies together, creating a more robust privacy protection system that is resistant to various privacy attacks while maintaining data utility for training.
Solution Approach 2:
The patent uses correlation analysis results as feedback to dynamically adjust the noise addition strategy. By continuously analyzing the correlation structure of model parameters and adjusting perturbation levels accordingly, the system adapts to potential attacks and maintains effective privacy protection. The feedback loop ensures that protection is strengthened where correlations indicate vulnerability.
Data Source
AI summary
A federated mining method for multimodal data based on multiple security policies includes: a federated learning framework is used as a data mining model for distributed data mining; a multiple authentication mechanism is designed; a local edge node is selected to participate in a federated computation to obtain authenticated local edge node and aggregate a dataset; a multimodal data fusion and a multimodal data classification are performed on the dataset by a generalized multimodal data feature fusion model based on a multi-headed attention mechanism; and an adaptive perturbation mechanism based on cyclic correlation analysis and differential privacy is designed to add noise round by round and dynamically. A federated mining method for multimodal data is further provided.