Learning Apparatus for Membership Inference Attack Resistance
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Solution Overview
Problem
Machine learning models are vulnerable to membership inference (MI) attacks, which can leak confidential information, and existing countermeasures either introduce noise affecting inference results or trade off accuracy and attack resistance, making it difficult to achieve high resistance and accuracy simultaneously.
Innovation Solution
A learning apparatus and method that divides learning data into multiple sets, generates inference devices using different subsets, and trains these devices to produce second learning data, which is then used to generate a highly resistant and accurate inference device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to inference results for protection, then resistance to MI attacks is improved, but inference accuracy deteriorates
Solution Approach 1:
The learning data is divided into multiple subsets, and multiple inference devices are generated by training on different subsets. This segmentation approach allows the system to achieve robustness against MI attacks while maintaining inference accuracy through ensemble reasoning, avoiding the need to add noise to individual inference results.
Solution Approach 2:
Multiple inference devices generated from different data subsets are combined through a learning device that integrates their outputs. This merging mechanism enables the system to achieve both high accuracy and strong MI attack resistance by aggregating insights from multiple specialized models without introducing noise.
2Reliability
If adversarial regularization is applied to improve attack resistance, then resistance to MI attacks is improved, but accuracy deteriorates
Solution Approach 1:
Instead of applying regularization that trades off accuracy, the system segments the learning data into multiple subsets and trains separate inference devices on each subset. This allows each device to specialize in different data regions while the learning device aggregates their results, achieving both accuracy and attack resistance without the accuracy penalty of traditional adversarial regularization.
Solution Approach 2:
The learning device acts as an intermediary that combines the outputs of multiple inference devices. This intermediary mechanism allows the system to leverage the strengths of individual models (high accuracy on their training subsets) while achieving overall robustness against MI attacks through ensemble reasoning, avoiding the need for accuracy-sacrificing regularization.
3Reliability
If multiple inference devices are generated from divided data, then resistance to MI attacks is improved, but device complexity increases
Solution Approach 1:
The learning device serves multiple functions: it aggregates results from multiple inference devices, performs final inference, and can be used for both accurate prediction and MI attack resistance. This multi-functionality reduces overall system complexity by consolidating multiple specialized models into a single versatile component that handles both accuracy and security requirements.
Data Source
AI summary
A learning apparatus according to the present example embodiment includes: a data dividing unit that generates n sets of divided data by dividing first learning data into n (n is an integer of 2 or more); an inference device generation unit that generates n inference devices for learning data generation by machine learning using data excluding one set of divided data from the first learning data; a learning data generation unit that generates second learning data by inputting the one set of the divided data excluded from the machine learning into each of the n inference devices for learning data generation; and a learning unit that generates a second inference device by machine learning using the second learning data.