Probabilistic Reasoning with LLMs for k-anonymity Estimation

• URL: http://arxiv.org/abs/2503.09674v1
• Date: Wed, 12 Mar 2025 17:41:25 GMT
• Title: Probabilistic Reasoning with LLMs for k-anonymity Estimation
• Authors: Jonathan Zheng, Sauvik Das, Alan Ritter, Wei Xu,
• Abstract summary: We introduce a novel numerical reasoning task under uncertainty, focusing on estimating the k-anonymity of user-generated documents containing privacy-sensitive information.<n>We propose BRANCH, which uses LLMs to factorize a joint probability distribution to estimate the k-value.<n>Our experiments show that this method successfully estimates the correct k-value 67% of the time, an 11% increase compared to GPT-4o chain-of-thought reasoning.
• Score: 23.16673184539629
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 novel numerical reasoning task under uncertainty, focusing on estimating the k-anonymity of user-generated documents containing privacy-sensitive information. We propose BRANCH, which uses LLMs to factorize a joint probability distribution to estimate the k-value-the size of the population matching the given information-by modeling individual pieces of textual information as random variables. The probability of each factor occurring within a population is estimated using standalone LLMs or retrieval-augmented generation systems, and these probabilities are combined into a final k-value. Our experiments show that this method successfully estimates the correct k-value 67% of the time, an 11% increase compared to GPT-4o chain-of-thought reasoning. Additionally, we leverage LLM uncertainty to develop prediction intervals for k-anonymity, which include the correct value in nearly 92% of cases.

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