Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders

• URL: http://arxiv.org/abs/2408.11046v1
• Date: Tue, 20 Aug 2024 17:55:15 GMT
• Title: Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders
• Authors: Yuan Xin, Zheng Li, Ning Yu, Dingfan Chen, Mario Fritz, Michael Backes, Yang Zhang,
• Abstract summary: We focus on the membership leakage of pre-training data exposed through downstream models adapted from pre-trained language encoders. Our evaluations reveal, for the first time, the existence of membership leakage even when only the black-box output of the downstream model is exposed.
• Score: 68.00224057755773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite being prevalent in the general field of Natural Language Processing (NLP), pre-trained language models inherently carry privacy and copyright concerns due to their nature of training on large-scale web-scraped data. In this paper, we pioneer a systematic exploration of such risks associated with pre-trained language encoders, specifically focusing on the membership leakage of pre-training data exposed through downstream models adapted from pre-trained language encoders-an aspect largely overlooked in existing literature. Our study encompasses comprehensive experiments across four types of pre-trained encoder architectures, three representative downstream tasks, and five benchmark datasets. Intriguingly, our evaluations reveal, for the first time, the existence of membership leakage even when only the black-box output of the downstream model is exposed, highlighting a privacy risk far greater than previously assumed. Alongside, we present in-depth analysis and insights toward guiding future researchers and practitioners in addressing the privacy considerations in developing pre-trained language models.

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