Internship Research / Limiting Information Leakage in LLM-Based Systems for Network Applications

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description . Context . Large language models are increasingly used to handle user requests that contain personal or sensitive information. While the model processes such a request, information from the input can be exposed beyond what is strictly needed to answer it. This raises concerns about privacy and data minimization. Privacy in LLMs has been studied from several angles: memorization and extraction of training data [1], what it means for a language model to preserve privacy [2], contextual integrity in LLM behavior [3], and privacy-preserving reasoning [4]. Objectives . The internship will study how LLM-based systems can complete a user’s request while limiting the exposure of sensitive information contained in it, with network applications as the target domain. The main questions are:. How can the information flows of an LLM-based system be modeled?. How can privacy leakage be formally defined and measured?. What mechanisms reduce leakage while preserving the quality of the result?. Work plan . Review the literature on privacy in LLMs and on privacy-preserving mechanisms, such as differential privacy [5]. Define a threat model and a privacy-leakage metric, and select a representative network use case. Design and implement an approach to reduce leakage. Evaluate it experimentally against baselines, along a privacy–utility trade-off. Candidate profile . Interest in privacy and security, w