大语言模型场景下个人数据定价研究——基于隐私补偿的方法  

Personal Data Pricing for Large Language Models Based on Privacy Compensation

作　　者：刘晓慧 陶成煦 许葳 吴江[1] Liu Xiaohui;Tao Chengxu;Xu Wei;Wu Jiang(School of Information Management,Wuhan University,Wuhan 430072,China;School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学信息管理学院,武汉430072 [2]武汉大学国家网络安全学院,武汉430072

出　　处：《数据分析与知识发现》2025年第3期106-116,共11页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金专项项目(项目编号:72442030);国家自然科学基金重点项目(项目编号:72232006)的研究成果之一。

摘　　要：【目的】为大语言模型场景下的个人数据定价提供一种量化隐私损失、补偿隐私价值的方法。【方法】基于前提假设,结合差分隐私,以方向统计学为基础,提出一种能够评估数据隐私值并量化数据价值的方法,进而利用SST-2数据集进行方法评估。【结果】在隐私参数与准确率、预算与准确率的关系方面,随着隐私参数的增加或预算的增加,模型准确率均会增加,证明了方法的有效性。【局限】数据集和模型架构的选择较为单一;在定价机制上仅考虑了隐私因素对价格的影响。【结论】所提方法能够评估数据的隐私值,并量化数据价值,为大语言模型下的个人数据定价提供支撑。[Objective]This paper provides a method for pricing personal data in the context of large language models by quantifying privacy loss and compensating for privacy value.[Methods]Based on the premise hypothesis and differential privacy,we proposed a method that evaluates the data privacy value and quantifies the data value using directional statistics.We assessed the new method with the SST-2 dataset.[Results]Regarding the relationship between privacy parameters and accuracy,as well as budget and accuracy,the model accuracy increases with the increase of privacy parameters or budget,demonstrating the new method’s effectiveness.[Limitations]The choice of dataset and model architecture is relatively limited.The pricing mechanism only considers the impact of privacy factors on the pricing.[Conclusions]The proposed method can evaluate the privacy value of data and quantify the data’s value,providing support for personal data pricing in the context of large language models.

关 键 词：数据要素 数据交易 个人数据 数据定价 隐私补偿 大语言模型 

分 类 号：G203[文化科学—传播学]