用大模型估算文本隐私风险，提升匿名化程度评估精度。
Probabilistic Reasoning with LLMs for k-anonymity Estimation
- 将个人数据概率分布分解为因子，通过贝叶斯网络分别估算
- 在测试中准确估计k值率达73%，比基线高13%
- 模型不确定性可反映预测可靠性，高方差结果准确率低37.47%
概率推理是人类与人工智能处理不确定性和模糊性决策的核心能力。本文提出一种面向大语言模型的新数值推理任务，聚焦于估算包含敏感信息的用户生成文档的隐私风险。我们提出BRANCH方法，用于估算文本的k-隐私值——即与给定信息匹配的人群规模。BRANCH将个人身份信息的联合概率分布分解为多个随机变量因子，分别利用贝叶斯网络估算每个因子的概率，并合并计算最终的k值。实验表明，该方法在73%的情况下成功估算出k值，较o3-mini结合思维链推理的方法提升13%。此外，我们发现模型不确定性是准确性的良好指标：高方差预测的平均准确率低37.47%。
原文摘要 · Abstract (English)
Probabilistic reasoning is a key aspect of both human and artificial intelligence that allows for handling uncertainty and ambiguity in decision-making. In this paper, we introduce a new numerical reasoning task under uncertainty for large language models, focusing on estimating the privacy risk of user-generated documents containing privacy-sensitive information. We propose BRANCH, a new LLM methodology that estimates the k-privacy value of a text-the size of the population matching the given information. BRANCH factorizes a joint probability distribution of personal information as random variables. The probability of each factor in a population is estimated separately using a Bayesian network and combined to compute the final k-value. Our experiments show that this method successfully estimates the k-value 73% of the time, a 13% increase compared to o3-mini with chain-of-thought reasoning. We also find that LLM uncertainty is a good indicator for accuracy, as high-variance predictions are 37.47% less accurate on average.
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