Neural Network Security via Data Scrambling for Membership Inference Defense
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Solution Overview
Problem
Deployed neural networks are vulnerable to membership inference attacks, where attackers can infer if data was part of the training data by analyzing model outputs, posing a threat to the confidentiality and security of the training data.
Innovation Solution
A system comprising a data scrambler and a descrambling layer is implemented to enhance the security of deployed neural networks. The data scrambler processes inputs only from authenticated and authorized entities using a scrambling algorithm, while the descrambling layer attached to the neural network descrambles the inputs to ensure correct model predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deployed neural network is made highly accurate by compressing training data information into the model, then the model can solve complex problems effectively, but the model becomes vulnerable to membership inference attacks that leak training data information
Solution Approach 1:
The patent introduces a scrambling layer as an intermediary component between the input data and the neural network. This layer transforms the input data through scrambling operations that prevent attackers from directly inferring training data membership, while still allowing the network to process the scrambled information effectively for accurate predictions.
Solution Approach 2:
The patent applies parameter changes by transforming the input data parameters through the scrambling layer. The scrambling operations modify the data representation (changing parameters such as feature distributions and relationships) in a way that protects against membership inference attacks while preserving the essential information needed for accurate model predictions.
2Reliability
If security measures are implemented during data acquisition and model training, then training data confidentiality is protected, but fewer precautions are taken during deployment leaving the model vulnerable to attacks
Solution Approach 1:
The patent implements preliminary action by integrating the scrambling layer into the model deployment process. The security measure is built into the deployment architecture itself, automatically scrambling inputs before they reach the neural network, rather than adding complex external security systems during deployment.
3Object-affected harmful factors
If authentication and authorization systems are added to control data access, then unauthorized access is prevented, but the system complexity and operational overhead increase
Solution Approach 1:
The patent applies self-service by making the scrambling operation intrinsic to the model's input processing. The model itself performs the security function through its built-in scrambling layer, eliminating the need for separate authentication and authorization systems. The security is self-executing as part of the normal inference process.
Data Source
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AI summary
Disclosed concepts provide improved security of a deployed neural network (i.e. improved security during inferencing of a neural network), in particular against membership inference attacks. Specifically, a data scrambler and a descrambling layer are provided. The data scrambler receives an input from an entity (e.g., a user) and only scrambles the input if the entity is authenticated and authorized. The descrambling layer is attached to the deployed neural network, and descrambles the input so that the neural network may provide a correct output corresponding to the initial (unscrambled) input from the entity. Of course, if the entity is not authenticated and/or authorized, then the input will not be scrambled and so the descrambling layer will provide an incorrect input to the neural network. Accordingly, unauthenticated, or unauthorized entities will not receive the correct output corresponding to the input from the entity from the deployed neural network (as the input provided by such entities will not be scrambled by the data scrambler).