Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships

• URL: http://arxiv.org/abs/2402.12189v2
• Date: Sun, 1 Sep 2024 03:02:36 GMT
• Title: Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships
• Authors: Myung Gyo Oh, Hong Eun Ahn, Leo Hyun Park, Taekyoung Kwon,
• Abstract summary: Neural language models (LMs) are vulnerable to training data extraction attacks due to data memorization. This paper introduces a novel attack scenario wherein an attacker fine-tunes pre-trained LMs to amplify the exposure of the original training data. Our empirical findings indicate a remarkable outcome: LMs with over 1B parameters exhibit a four to eight-fold increase in training data exposure.
• Score: 3.544065185401289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural language models (LMs) are vulnerable to training data extraction attacks due to data memorization. This paper introduces a novel attack scenario wherein an attacker adversarially fine-tunes pre-trained LMs to amplify the exposure of the original training data. This strategy differs from prior studies by aiming to intensify the LM's retention of its pre-training dataset. To achieve this, the attacker needs to collect generated texts that are closely aligned with the pre-training data. However, without knowledge of the actual dataset, quantifying the amount of pre-training data within generated texts is challenging. To address this, we propose the use of pseudo-labels for these generated texts, leveraging membership approximations indicated by machine-generated probabilities from the target LM. We subsequently fine-tune the LM to favor generations with higher likelihoods of originating from the pre-training data, based on their membership probabilities. Our empirical findings indicate a remarkable outcome: LMs with over 1B parameters exhibit a four to eight-fold increase in training data exposure. We discuss potential mitigations and suggest future research directions.

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