Attack-Resistant Inference Models Using One-Way Transformed Data
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Solution Overview
Problem
Inference models are vulnerable to unauthorized access of sensitive input data, leading to potential data breaches and compliance issues with varying data privacy regulations.
Innovation Solution
Deploy input data attack resistant inference models trained using transformed input data via a one-way function, ensuring that inferences generated are resistant to reconstruction of the original data, thus protecting sensitive information while maintaining service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models use original input data, then inference quality is maintained, but data security and privacy compliance deteriorate
Solution Approach 1:
The patent introduces transformed input data as an intermediary between the original sensitive input data and the inference model. This transformed data serves as a mediator that allows the model to generate accurate inferences without direct access to the original sensitive data, thereby maintaining inference quality while reducing data breach risk
Solution Approach 2:
The patent creates a copy of the input data that has been transformed through a one-way function. This copied and transformed data is then used by the inference model instead of the original data. The copy maintains the necessary information for accurate inference while the one-way transformation ensures the original sensitive data cannot be reconstructed, thus preserving inference quality while protecting data security
2Object-affected harmful factors
If input data is transformed using one-way function, then data security is improved, but inference model performance may deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-transforming the input data using a one-way function before feeding it to the inference model. The transformation is performed in advance with the specific purpose of enabling secure inference. The inference model is then trained on this transformed data, so the transformation doesn't hinder performance - it becomes the expected input format, thus achieving both security and maintained inference accuracy
Solution Approach 2:
The patent changes the parameter state of the input data by applying a one-way transformation function. This parameter change transforms the data into a different representation that preserves the information needed for inference while removing the ability to reconstruct the original sensitive data. The inference model adapts to this new parameter state, maintaining accuracy while improving security
3Reliability
If transformed input data is used, then compliance with data privacy regulations is improved, but system complexity increases
Solution Approach 1:
The transformed input data acts as an intermediary that simplifies compliance while managing complexity. Rather than implementing complex access control and monitoring systems to protect original data, the patent uses transformation as a straightforward mechanism that inherently protects data. The intermediary transformed data meets regulatory requirements without requiring complex additional security infrastructure
Data Source
AI summary
Methods and systems for managing use of inference models are disclosed. To manage use of inference models, transformed input data may be obtained, the transformed input data being generated using input data and a one-way function to reduce a level of sensitivity of the input data and the level of sensitivity indicating a level of impact of unauthorized access to the input data. A reconstruction resistant inference may be generated using the transformed input data and an input data attack resistant inference model. The input data attach resistant inference model may be trained to ingest the transformed input data so that inferences generated by the input data attack resistant inference model have a reduced likelihood of being usable to reconstruct the input data. Computer-implemented services may be provided based on the reconstruction resistant inference.