Dual-Subsystem AI Isolation for Data Confidentiality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing systems face challenges in maintaining data confidentiality, availability, and integrity, particularly in scenarios where artificial intelligence models are shared or trained using user input, leading to potential exposure or leakage of private data.
Innovation Solution
A computing system is designed with a dual-subsystem architecture, comprising a public subsystem for network connectivity and a private subsystem for isolated AI operations, utilizing a power adapter to selectively power one subsystem at a time, and a memory storage device for secure data transfer between the subsystems, enhancing data confidentiality and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is shared with other users and systems for AI model training and output generation, then AI model functionality and adaptability are improved, but data confidentiality and security deteriorate
Solution Approach 1:
The system is divided into two separate subsystems: a public subsystem that connects to the network for data input and a private subsystem that executes AI models in isolation. This segmentation allows data to be shared for model training while maintaining confidentiality during execution, resolving the contradiction between AI model functionality and data confidentiality.
Solution Approach 2:
A memory storage device acts as an intermediary between the public and private subsystems, enabling secure data transfer. The intermediary facilitates AI model functionality by allowing data to move from the public to private subsystem while maintaining security boundaries, thus preserving data confidentiality during the transfer and execution process.
2Reliability
If a dual-subsystem architecture with isolation is implemented, then data confidentiality is improved, but device complexity increases
Solution Approach 1:
The public and private subsystems are merged into a single computing device with shared components such as power supply, housing, and display. This merging approach maintains data confidentiality through logical isolation while reducing device complexity by eliminating the need for completely separate physical systems.
Solution Approach 2:
Shared components like the power adapter, display, and housing serve multiple functions for both the public and private subsystems. This multi-functionality reduces the overall device complexity while maintaining the security benefits of subsystem isolation, as the same physical infrastructure supports both confidential and non-confidential operations.
3Reliability
If selective power supply to subsystems is implemented, then data integrity during transfer is improved, but power management complexity increases
Solution Approach 1:
The power adapter automatically detects which subsystem requires power and directs electrical energy accordingly without external intervention. This self-service capability ensures data integrity during transfers by powering only the active subsystem while simplifying power management, as the system autonomously handles the complexity of selective power distribution.
Data Source
AI summary
A device including a public subsystem comprising a first processor set of processors and a first memory set of memories storing first instructions for a first operating system. The device including a private subsystem comprising a second processor set of processors and a second memory set of memories storing second instructions for a second operating system. The device including a first interface communicatively coupled with the public subsystem and a second interface communicatively coupled with the private subsystem, each interface configured to interconnect the subsystems. The public subsystem configured to present information on a display and connect with a network. The private subsystem configured to present information on the display, incapable of connecting with the network, and configured to receive, from the public subsystem via the second interface, data and execute, locally by the second processor set, instructions based on the data.