Network Architecture for Local AI Model Protection and Privacy
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Solution Overview
Problem
Cloud computing environments face challenges such as high costs, network latency, exclusion of offline use, and privacy concerns, particularly when executing privacy-sensitive content that may violate regulations like GDPR due to data transmission for processing.
Innovation Solution
Client devices are configured to extend cloud computing environments, allowing secure deployment and execution of AI models locally, with attestation and provisioning capabilities to protect AI model IP and ensure privacy, using trusted runtime environments and encryption to isolate and secure AI workloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing environments are used to execute AI models and process content, then processing capabilities and functionality are improved, but network latency increases and privacy concerns arise due to data transmission
Solution Approach 1:
The system segments the cloud computing function by deploying AI models and processing capabilities at the network edge (e.g., gateways, routers, edge devices) rather than requiring all processing to occur in centralized cloud data centers. This segmentation brings computation closer to data sources, reducing network latency while maintaining cloud-like processing capabilities.
Solution Approach 2:
The patent introduces a new dimension in the computing architecture by adding an edge computing layer between end devices and centralized cloud. This dimensional addition allows processing to occur at multiple levels (edge and cloud simultaneously), enabling low-latency local processing while maintaining cloud connectivity for less time-sensitive operations.
2Productivity
If cloud computing environments are used to execute AI models, then processing capabilities are improved, but costs increase for cloud service providers
Solution Approach 1:
By segmenting workloads and deploying AI models at the network edge, the system reduces the volume of data that must be transmitted to and processed in centralized cloud environments. This segmentation allows cloud service providers to serve fewer, more critical workloads while edge devices handle routine processing, thereby reducing overall cloud computing costs.
Solution Approach 2:
The patent employs model copying by distributing replicated versions of AI models across multiple edge devices and network nodes. Instead of relying on a single centralized cloud instance, multiple edge devices maintain local copies of the models, enabling parallel processing and reducing the computational burden on any single cloud infrastructure.
3Adaptability or versatility
If cloud computing environments are used to process private or classified content, then functionality is improved, but privacy and confidentiality risks increase due to data transmission and processing in the cloud
Solution Approach 1:
The system applies local quality by processing private and classified content locally at edge devices and network nodes rather than transmitting it to centralized cloud environments. Sensitive data remains within trusted local boundaries while still benefiting from advanced AI processing capabilities, thereby eliminating privacy and confidentiality risks associated with cloud data transmission.
4Loss of time
If AI models are deployed to client devices for local execution, then network latency is reduced and offline use is enabled, but security and integrity protection of AI models becomes more challenging
Solution Approach 1:
The patent introduces a security intermediary layer that includes cryptographic protection mechanisms, trust anchors, and verification systems between the cloud and edge devices. This intermediary ensures that AI models deployed to client devices maintain security and integrity by validating their authenticity and protecting them from tampering, while still enabling local execution and reduced latency.
Solution Approach 2:
The system changes the security parameters by implementing dynamic encryption, key management, and access control mechanisms that adapt to the deployment environment. AI models are encrypted with keys managed by trusted runtime environments, and access rights are dynamically adjusted based on device trust levels, ensuring security while maintaining functionality.
Data Source
AI summary
A remote (e.g., cloud, edge-cloud) computing environment may be improved by configuring a client device to function as an extension of the cloud computing environment, such as by coordinating the execution and processing of data on the client device. Secure content or privacy-sensitive content is protected confidentially while in a cloud computing environment or client device, and may be moved to a client device and decrypted to a client local container for various user productivity processing workloads, thereby reducing or eliminating privacy concerns. This solution also provides improved security for sharing and usage of pre-trained AI models on a client device, such as by extending attestation and provisioning capabilities from cloud computing environment into clients, and by leveraging cloud computing technology on clients that protects cloud assets. This provides an improved ability to protect trained AI models and avoid exposing an AI model training process on a client device.