Secure Evolution as a Service via Segmented Data Firewall
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Solution Overview
Problem
There is a need to secure domain-specific data sets and code from unauthorized access in Evolution-as-a-Service (EaaS) processes, where third-party vendors need to protect their algorithms and customers need to safeguard their data, especially in AI applications involving multiple parties.
Innovation Solution
Implementing a firewall separation between domain-specific data sets and the evolution service, allowing customers to use evolution services securely while keeping the evolution algorithms and code protected, by transmitting candidate individuals with unique identifiers across servers, ensuring secure data and intellectual property protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a third-party vendor provides Evolution-as-a-Service to generate candidate code or models, then customers can access optimized solutions for their domain, but the vendor's evolutionary computing processes and algorithms become vulnerable to customer access
Solution Approach 1:
The system segments the evolutionary computing process into distinct components: the vendor's evolution service that generates candidate code, and the customer's evaluation environment that tests candidates against their data. This segmentation allows customers to access optimized solutions while the vendor maintains control and protection of their algorithms through separate execution environments.
Solution Approach 2:
The patent introduces an intermediary evaluation environment that acts as a mediator between the vendor's evolution service and customer data. This intermediary allows candidate code to be tested against customer datasets without direct access to either the vendor's algorithms or the customer's sensitive data, resolving the contradiction between service accessibility and algorithm protection.
2Productivity
If customer data sets are made accessible to third-party vendors for AI processing, then evolution services can be provided, but customer data including competitive and health-related information becomes vulnerable
Solution Approach 1:
The evaluation environment serves as an intermediary that enables evolution services to be delivered while protecting customer data. The environment allows candidate code to interact with customer datasets through controlled interfaces, preventing direct access to sensitive information while still enabling the evolution process to proceed productively.
Solution Approach 2:
The system implements local quality by creating a specialized evaluation environment with specific security properties tailored to customer data protection needs. This environment has controlled access rules and isolation properties that protect sensitive data while still allowing necessary computational operations to occur.
3Reliability
If firewall separation is implemented between data sets and evolution service, then security and privacy are maintained, but system complexity increases
Solution Approach 1:
The evaluation environment is designed as a universal platform that can handle multiple customer datasets and evolution services while maintaining security. This multi-functional environment reduces overall system complexity by providing a standardized secure interface that works across different scenarios, rather than requiring separate firewall configurations for each case.
Data Source
AI summary
Described herein is a process which facilitates segmented security between domain-specific data sets being evaluated as part of a candidate evaluation service and third-party evolution services, wherein the data sets are not transmitted to the evolution service which is evolving candidates for evaluation. This enables customers with secure data sets to use candidate evolution services securely by obtaining a population of potentially optimal candidate models to evaluate and then optimizing on those data sets in their own secure fashion.