Federated Generative Models for Private Website Risk Assessment
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Solution Overview
Problem
Existing systems lack the ability to perform privacy and security assessments of websites without central data aggregation, failing to consider individual user behavior and provide tailored, efficient, and relatable risk assessments.
Innovation Solution
A federated learning approach using generative adversarial networks (GANs) trained at the user-end to simulate user interactions, allowing for personalized risk assessments while keeping data private, and updating websites to mitigate identified risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized data aggregation is used for website risk assessment, then comprehensive risk analysis can be performed, but user privacy is compromised
Solution Approach 1:
The system segments the centralized risk assessment process into distributed federated learning nodes, where each user's device performs local model training on their own data. The global model is aggregated from multiple local models without centralizing user data, thus maintaining assessment accuracy while protecting user privacy.
Solution Approach 2:
The patent introduces a federated learning server as an intermediary that coordinates model training and aggregation without accessing user data. The server facilitates communication between users and the global model while ensuring data remains localized, acting as a mediator that enables comprehensive analysis without direct data exposure.
2Measurement precision
If user-specific risk assessments are implemented, then assessment relevance improves, but computational resources at user-end increase
Solution Approach 1:
The system implements dynamic model training where the risk assessment model adapts to each user's specific behavior patterns and preferences. The model dynamically adjusts to user-specific characteristics while the federated learning framework dynamically balances local training requirements with global model constraints, enabling personalized assessments without overwhelming user devices.
Solution Approach 2:
Instead of implementing complete local risk assessment systems on user devices, the patent applies partial action by training only the necessary components of the model locally and relying on the aggregated global model for comprehensive analysis. This approach provides user-specific relevance without requiring full computational capabilities at the user end.
3Object-affected harmful factors
If federated learning is used to maintain data privacy, then user data security improves, but model training efficiency decreases
Solution Approach 1:
The system performs preliminary local model training at each user's device before aggregation, preparing the model in advance with user-specific data characteristics. This preliminary action reduces the computational burden during the aggregation phase and accelerates overall training convergence while maintaining data privacy throughout the process.
Solution Approach 2:
The federated learning process implements continuous model improvement through iterative training cycles where the global model is continuously refined by incorporating insights from multiple users. This continuous useful action maintains training efficiency by keeping the model constantly improving without requiring complete retraining, thus balancing privacy protection with productivity.
Data Source
AI summary
A method, computer program, and computer system are provided for predicting and assessing risks on websites. Data corresponding to historical interactions of a user with one or more websites is accessed. A simulation of actions of the user is generated based on the accessed data, and actions of the user are simulated on a pre-defined target website based on the generated simulation of the actions of the user. Risks on the target website are identified based on simulating the actions of the user. The website is updated to mitigate the identified risks.