Subject-Level Privacy Attack Analysis for Federated Learning
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Solution Overview
Problem
Federated learning models are vulnerable to subject-level inference attacks, which can reveal private information from the training data, especially in cross-silo settings where data is distributed across multiple users, necessitating a more precise approach to protect individual data privacy beyond item-level and user-level privacy.
Innovation Solution
Implementing subject-level privacy attack analysis techniques, such as membership inference, loss across rounds, and loss across neighborhood attacks, to assess the vulnerability of federated machine learning models and provide insights for mitigating strategies like differential privacy to obscure training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated learning models are trained with data distributed across multiple users, then the model can learn from diverse data sources and improve performance, but the model becomes vulnerable to subject-level inference attacks that can reveal private information
Solution Approach 1:
The patent segments privacy protection into multiple levels: item-level privacy (individual data points), user-level privacy (each user's data set), and subject-level privacy (cross-user individual identification). This multi-layered segmentation allows the system to protect different aspects of privacy independently, addressing the vulnerability to subject-level inference attacks while maintaining the benefits of distributed data training.
Solution Approach 2:
The patent introduces subject-level privacy as an intermediary layer between user-level privacy and individual data protection. By adding this intermediate protection layer, the system can prevent direct inference attacks on individual subjects while still allowing federated learning to utilize distributed data across multiple users, thus resolving the contradiction between data utilization and privacy protection.
2Reliability
If subject-level privacy protection is enhanced to prevent inference attacks, then individual data privacy is better protected, but the complexity of the federated learning system increases
Solution Approach 1:
The patent implements subject-level privacy protection as a preliminary measure during the federated learning training process. By pre-establishing subject-level privacy boundaries and protection mechanisms before inference attacks can occur, the system enhances privacy reliability without requiring complex real-time detection and response systems, thus managing system complexity more effectively.
Solution Approach 2:
The patent changes the privacy protection parameter from traditional user-level or item-level to subject-level, which fundamentally alters how privacy is measured and enforced in federated learning. This parameter change enables more precise privacy control that directly addresses inference attack vulnerabilities while maintaining manageable system complexity through a unified privacy framework.
Data Source
AI summary
Subject level privacy attack analysis for federated learning may be performed. A request that selects an analysis of one or more inference attacks may be received to determine a presence of data of a subject in a training set of a federated machine learning model. The selected inference attacks may be performed to determine the presence of the data of subject in the training set of the federated machine learning model. Respective success measurements may be generated for the selected inference attacks based on the performance of the selected inference attacks, which may then be provided.