TLDR: This research introduces a framework for “data minimization” in Large Language Model (LLM) prompting, aiming to quantify and achieve the least privacy-revealing disclosure while maintaining task utility. It uses a priority-queue tree search to transform user prompts by redacting, abstracting, or retaining sensitive information. Findings show that more capable LLMs can tolerate significantly stronger data minimization, but current LLMs struggle to predict optimal minimization directly, often oversharing and favoring abstraction over more protective redaction. The study highlights data minimization as a promising paradigm for LLM input privacy and calls for further research into model-specific predictors.
The rapid rise of large language models, or LLMs, in everyday applications has brought incredible convenience, but also a growing concern: privacy. When we interact with these powerful AI tools, we often share personal information, sometimes more than is truly necessary. This oversharing can lead to various privacy risks, from the AI remembering sensitive details to potential security breaches.
A new research paper, titled “OPERATIONALIZINGDATAMINIMIZATION FOR PRIVACY-PRESERVINGLLM PROMPTING,” tackles this challenge head-on. Authored by Jijie Zhou and Tianshi Li from Northeastern University, and Niloofar Mireshghallah from Carnegie Mellon University, the paper introduces a groundbreaking framework to formally define and implement “data minimization” in LLM prompting. The core idea is to figure out the absolute minimum amount of personal information needed in a user’s prompt to get a useful response, thereby maximizing privacy.
Understanding Data Minimization
Data minimization is a well-established principle in privacy, advocating for limiting the collection of personal information to only what is essential for a specific purpose. While this concept has been around, applying it rigorously to LLM interactions has been a complex task. This research provides a formal way to quantify and achieve this balance between utility and privacy.
The framework works by transforming user prompts. For any sensitive piece of information detected in a prompt, the system can take one of three actions: RETAIN (keep it as is), ABSTRACT (replace it with a general category, like “New York” becoming “a city in the U.S.”), or REDACT (completely remove it, like “New York” becoming “[GEOLOCATION]”). These actions are ordered by their privacy strength, with redaction offering the highest level of protection.
How It Works: The Priority-Queue Tree Search
To find the optimal balance, the researchers developed a clever algorithm called a priority-queue tree search. Instead of simply trying every possible combination, this search method systematically explores different versions of a prompt, starting with the most privacy-preserving (most redacted/abstracted) and gradually adding more information only if it’s absolutely necessary to maintain the quality of the LLM’s response. This ensures that the final prompt is as private as possible without sacrificing the usefulness of the AI’s output.
Key Findings: Frontier Models Lead the Way
The evaluation of this framework across various datasets and nine different LLMs yielded fascinating results. One of the most significant findings is that larger, more advanced “frontier” LLMs, such as GPT-5, are much more capable of handling heavily minimized prompts while still delivering high-quality responses. For instance, on open-ended conversations, GPT-5 could tolerate an impressive 85.7% redaction and 8.6% abstraction, meaning only a tiny fraction of the original sensitive information needed to be retained. In contrast, smaller models like Qwen2.5-0.5B required retaining a much larger portion of the original data (69.7%). This suggests that more powerful AI models have a greater inherent understanding of context, allowing them to infer what’s needed even from highly sanitized inputs.
Interestingly, the study also found that data minimization primarily relies on redaction. Abstraction, while useful, typically accounts for a smaller percentage of transformations, indicating that simply deleting sensitive spans is often sufficient to preserve utility.
The Prediction Gap: LLMs Struggle to Self-Minimize
Beyond establishing these “oracles” of data minimization, the research also explored whether LLMs could *directly predict* the optimal level of minimization without the guided search. The results were clear: LLMs, from small to frontier models, are not good at this. They tend to “overshare,” meaning their direct predictions are less privacy-preserving than what the oracle framework achieves. There’s a noticeable bias towards abstraction over redaction, even when redaction would be more protective and still maintain utility. This highlights a “capability gap” – LLMs may not fully understand what information is truly essential for a task, leading them to be overly cautious and retain or abstract more than necessary.
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Looking Ahead
This research marks a significant step towards making LLM interactions more private by design. It demonstrates that data minimization is a viable and promising strategy, especially with the increasing capabilities of advanced LLMs. The findings also call for further research into developing model-specific predictors for data minimization and understanding the underlying reasons why LLMs struggle to identify truly necessary information. This work lays crucial groundwork for both advancements in machine learning and interdisciplinary studies on human-AI interaction and privacy. You can read the full paper here: OPERATIONALIZINGDATAMINIMIZATION FOR PRIVACY-PRESERVINGLLM PROMPTING.
