Private AI Data Exchange via Secure Real-Time Network Mediation
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Solution Overview
Problem
Entities operating in the AI and ML space are increasingly specialized and wary of sharing data or models via public networks due to security risks, leading to isolation and a lack of awareness of potential collaboration partners.
Innovation Solution
A private, real-time, and secure network is established using an AI controller to connect AI-related entities via AI on-ramps, leveraging existing data center architecture and implementing software-defined networking to facilitate secure data and model exchange, allowing hybrid use of public and private resources for enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If entities use public networks to share data and models, then collaboration and resource utilization improve, but security risks and data exposure increase
Solution Approach 1:
The patent introduces a private network as an intermediary between entities, allowing data and model sharing without direct public network exposure. The private network acts as a secure mediator that enables collaboration while isolating entities from public security threats, resolving the contradiction between collaboration capability and security risk.
2Reliability
If entities establish private networks for secure communication, then security and data protection improve, but network isolation and discovery difficulty worsen
Solution Approach 1:
The patent employs a private network as an intermediary that maintains security while enabling controlled discovery. Entities can be discovered and connected through the private network infrastructure without exposing themselves to public networks, thus preserving both data security and entity discoverability.
3Reliability
If specialized entities operate independently, then security and data protection improve, but collaboration opportunities and resource utilization worsen
Solution Approach 1:
The patent merges multiple specialized entities into a private network ecosystem, allowing them to maintain operational independence while combining their resources and capabilities. This enables secure data protection through isolation while improving resource utilization through coordinated collaboration within the private network.
4Ease of operation
If entities use public internet for communication, then ease of connection and discovery improve, but network security and data privacy worsen
Solution Approach 1:
The private network serves as an intermediary that simplifies connection processes while maintaining security. Entities can easily discover and connect to each other through the private network infrastructure without the security risks of public internet, thus improving ease of operation while protecting against network security threats.
Data Source
AI summary
In an embodiment, a method provides an environment for privately exchanging data for AI tasks. Identification of a task to perform, and a characteristic describing data needed to execute the task, is received. A data provider within the environment is located that has access to a data set according to the characteristic. A task provider within the environment is located. The located task provider is configured to execute the task. A real-time, private, and secure network connection between the data provider and the task provider is established. The established connection is configured such that the data provider and the task provider are able to communicate via the network connection without using publicly accessible network addresses. The data set is transferred from the data provider to the task provider via the established network connection. In response to the transfer, the task provider executes the task using the data set.