Chatbot Privacy Defense via Fake Attacker Model Fine-Tuning
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Solution Overview
Problem
Social chatbots based on large language models, such as GPT-2, face issues with memorization and overlearning, leading to personal privacy leaks during black-box personal privacy inference attacks, and existing solutions fail to prevent these leaks without compromising the chatbot's performance.
Innovation Solution
A computer-implemented method using a processor to train a Language Model (LM) with utility objectives and apply defense objectives, such as KL Loss and MI Loss, to fine-tune the model with a fake attacker model and pre-defined attributes, ensuring that personal privacy is not predicted by external predictors, thereby enhancing the chatbot's security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a language model is trained to improve its utility performance, then the chatbot's functionality is enhanced, but the model memorizes training data and personal privacy can be recovered via extraction attacks
Solution Approach 1:
The patent applies preliminary anti-action by introducing defense objectives during the training phase that actively counteract potential privacy leakage. The defense objectives are formulated to prevent the model from memorizing sensitive information before the actual privacy attack occurs, thereby neutralizing the threat in advance while maintaining utility performance.
Solution Approach 2:
The patent changes the training parameters by adding defense objectives to the loss function. This modifies the optimization landscape to balance utility improvement with privacy protection, preventing the model from overfitting to sensitive training data while still learning useful patterns for chatbot functionality.
2Reliability
If differential privacy and unlikelihood training are applied to mitigate memorization, then privacy protection is improved, but the chatbot's performance may be degraded
Solution Approach 1:
The patent modifies the training parameters by formulating defense objectives that are integrated into the existing training framework. This approach adjusts the optimization balance to protect privacy without significantly degrading chatbot performance, unlike traditional differential privacy methods that add noise and reduce utility.
Solution Approach 2:
The patent implements feedback mechanisms where the defense objectives provide continuous guidance during training to balance privacy and utility. The training process monitors both utility metrics and privacy risks, adjusting parameters dynamically to maintain optimal performance while preventing memorization.
3Reliability
If the model is fine-tuned with defense objectives and a fake attacker model, then privacy inference attack accuracy is reduced, but the training complexity increases
Solution Approach 1:
The patent introduces a fake attacker model as an intermediary during training. This intermediary simulates potential privacy attacks and provides feedback to the main model, enabling the model to learn defensive patterns without requiring complex adversarial training frameworks or multiple attacker models.
Solution Approach 2:
The patent performs preliminary action by training the model with defense objectives before deployment. The fake attacker model is used during this preliminary training phase to prepare the model against inference attacks, simplifying the overall process compared to continuous adversarial training after deployment.
Data Source
AI summary
A computer-implemented method for preventing leaking a personal privacy from a chatbot under black-box personal attribute inference attack is provided. The chatbot is provided via a neural network executed by a processor of a server. The method includes: training, by the processor, a Language Model (LM) of the chatbot according to utility objectives; applying, by the processor, one or more defense objectives with personal attribute predictor to fine-tune a target LM of the chatbot by using a fake attacker model and pre-define attributes with annotated datasets; and using, by the processor, the target LM on the chatbot to defend inference attack, such that the personal privacy of content inputted and sent to the chatbot cannot be predicted by external predictor and a security level of the chatbot is assured.