Autonomous User Membership Inference for Recommendation Privacy
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Solution Overview
Problem
Existing recommendation systems face challenges in ensuring user data privacy as they train models using user interactions without explicit permission, and constructing a reliable shadow model for membership inference attacks is challenging due to unknown model architecture and data distribution.
Innovation Solution
Utilize autonomous users, or sock puppets, to simulate real user interactions and generate interaction data, which is used to train an attack classifier to infer whether a target user's data was used to train the recommendation system, employing a binary classifier like a multiplayer perceptron or logistic regression model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user interaction data is used to train recommendation systems without explicit permission, then the recommendation system's accuracy and personalization improve, but user data privacy is compromised
Solution Approach 1:
The patent performs membership inference attacks before actual data training occurs. By simulating the training process with shadow models and testing whether test data members can be identified, the system proactively detects privacy risks before real user data is compromised, allowing preventive measures to be taken
Solution Approach 2:
The patent creates shadow models that copy the architecture and training process of the target recommendation system. These shadow models are trained on synthetic or publicly available data to replicate the behavior of the actual system, enabling privacy risk assessment without accessing or compromising real user data
2Reliability
If shadow models are constructed for membership inference attacks, then privacy risk detection capability improves, but model construction complexity increases due to unknown target model architecture
Solution Approach 1:
The patent employs multiple shadow models with different architectures, hyperparameters, and training configurations. By varying these parameters across multiple models and aggregating their results, the system overcomes the limitation of not knowing the target model's exact architecture, improving detection reliability while managing complexity through systematic parameter exploration
Solution Approach 2:
The patent develops a universal shadow model framework that can adapt to different recommendation system architectures. The shadow models are designed to be architecture-agnostic, using general machine learning approaches that can be applied across various recommendation systems, thereby reducing construction complexity while maintaining broad applicability
3Reliability
If multiple autonomous users are deployed to generate training data, then the robustness of the attack classifier improves, but computational resources and time requirements increase
Solution Approach 1:
The patent pre-generates interaction data for autonomous users before the actual membership inference attack. By creating and storing synthetic interaction datasets in advance, the system avoids time-consuming data generation during the attack phase, reducing computational overhead and execution time while maintaining classifier robustness
Solution Approach 2:
The autonomous users in the patent are self-contained computational entities that independently generate their own interaction data following predefined behavior patterns. Each autonomous user autonomously creates realistic interaction sequences without requiring external intervention or coordination, streamlining the data generation process and reducing overall computational complexity
Data Source
AI summary
Techniques for conducting membership inference attacks are disclosed. In an example, a plurality of target interactions of a target user with an item providing platform are monitored. A plurality of target recommendations for the target user is received from a recommendation system of the item providing platform. Using an attack classifier and based on (i) the plurality of target interactions and (ii) the plurality of target recommendations, an inference is made as to whether at least a subset of the plurality of target interactions and/or at least a subset of the plurality of target recommendations were used to train the recommendation system. The attack classifier is trained using training data associated with a plurality of autonomous users (such as autonomous sock puppets) interacting with the item providing platform. In an example, the item providing platform is one of a video providing platform, an audio providing platform, or a shopping platform.