认知追踪数据轨迹:使用累积差异得分审核判别语言模型中的数据出处

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhirui Zeng, Jialing He, Tao Xiang, Ning Wang, Biwen Chen, Shangwei Guo
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引用次数: 0

摘要

某些实体在未经授权的情况下获取和使用个人文本数据(如社交媒体评论和搜索历史)的做法日益增多,这已成为一种明显的趋势。为了维护数据保护法规(如亚太隐私倡议(APPI))并识别未经许可利用个人数据的情况,我们提出了一个新颖高效的审计框架,帮助用户进行认知分析,以确定其文本数据是否被用于数据增强。特别是,我们将重点放在使用 BERT 作为文本判别骨干的审计模型上,这些模型是流行在线服务的核心。我们首先提出了累积差异分数,它不仅涉及目标模型对审核样本的响应,还涉及预训练模型和微调模型之间的响应,以识别成员身份。根据我们的框架,我们实现了两种类型的审核方法(即样本级和用户级),并在两个下游应用上进行了综合实验以评估其性能。实验结果表明,样本级审核的 AUC 为 89.7%,准确率为 83%,而用户级方法的 AUC 为 89.7%,准确率为 88%。此外,我们还分析了增强方法如何影响审核性能,并阐述了这些观察结果的根本原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score

Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score

The burgeoning practice of unauthorized acquisition and utilization of personal textual data (e.g., social media comments and search histories) by certain entities has become a discernible trend. To uphold data protection regulations such as the Asia–Pacific Privacy Initiative (APPI) and to identify instances of unpermitted exploitation of personal data, we propose a novel and efficient audit framework that helps users conduct cognitive analysis to determine if their textual data was used for data augmentation. and training a discriminative model. In particular, we focus on auditing models that use BERT as the backbone for discriminating text and are at the core of popular online services. We first propose an accumulated discrepancy score, which involves not only the response of the target model to the auditing sample but also the responses between pre-trained and finetuned models, to identify membership. We implement two types of audit methods (i.e., sample-level and user-level) according to our framework and conduct comprehensive experiments on two downstream applications to evaluate the performance. The experimental results demonstrate that our sample-level auditing achieves an AUC of 89.7% and an accuracy of 83%, whereas the user-level method can audit membership with an AUC of 89.7% and an accuracy of 88%. Additionally, we undertake an analysis of how augmentation methods impact auditing performance and expound upon the underlying reasons for these observations.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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