Local Encoder Networks for Privacy-Preserving Remote AI Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI platform workflows raise data security and privacy concerns as real data is transmitted over the internet for training and deploying machine learning models, necessitating a solution to ensure secure data transmission and privacy.
Innovation Solution
Generating a unique neural network on a local device to encode training data into a secured format for transmission to a remote AI platform, using a user-specific encoder network to convert natural language data into high-dimensional embeddings, ensuring privacy through random number-based parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real data is transmitted over the internet from user to AI platform for training and deploying machine learning models, then the AI platform can provide intelligent services and improve hardware utilization, but data security and privacy are compromised
Solution Approach 1:
The system performs preliminary encoding of the data before transmission. An encoder network is generated and used to encode the training data into encoded training data before it is sent to the remote AI platform. This preliminary action ensures that the data is protected before it leaves the local device, preventing unauthorized access to sensitive information during transmission and storage.
Solution Approach 2:
The encoder network acts as an intermediary between the raw training data and the AI platform. Instead of transmitting plain text or structured data directly, the encoder transforms it into an encoded format that serves as a mediator - the AI platform can process the encoded data to train models, but cannot easily interpret or misuse the original sensitive information.
2Object-affected harmful factors
If data is encoded using a neural network before transmission, then data privacy is protected by transforming it into unreadable format, but the complexity of the system increases
Solution Approach 1:
The encoder network is designed to be self-contained and self-managing. Once generated, it automatically performs the encoding operation without requiring complex external management systems. The encoder network handles its own execution and data transformation, reducing the need for additional complex infrastructure to manage the encoding process.
Solution Approach 2:
The system changes the parameters of the data by transforming it from its original format into encoded representations. By changing the parameter space in which the data exists, the system achieves privacy protection through mathematical transformation rather than through complex physical or organizational measures. The encoded data maintains its functional properties while changing its representational form.
Data Source
AI summary
A method, computer system, and a computer program product for data protection is provided. The present invention may include, generating an encoder network. The present invention may also include, encoding a training data using the generated encoder network, wherein the training data includes natural language data. The present invention may further include, training a deep learning model using the encoded training data.